Saṃvega: The Jolt That Rearranged My Priorities

The short version

• Saṃvega is a Pali word for the jolt you feel when the fragility of life stops being an idea and shows up at your door. My teacher summed it up in two words: urgency and effort.
• Mine arrived during my mother's lung cancer workup. All the money in the world could not buy back her health, and I had given my best hours to work for years.
• Research backs up what I felt. When time feels short, people of every age turn toward what matters emotionally. Specific, personal reflection on death makes people less grasping. Strong relationships predict longer survival.
• The jolt fades fast. Intentions only partly turn into action, and the regrets that last longest come from what we never did.

The takeaway: don't wait for the goad to reach the bone. When it touches you, feel it, investigate it, and take one concrete step while it still stings.

saṃvega

Pali  /  say it sahm-vay-gah

The jolt that shows you, all at once, how far your days have drifted from what you value.

urgencyeffort

There is a word for the moment your priorities rearrange themselves without asking your permission. I learned it at a Buddhist teaching session, and it named something I had carried for months without a name.

The punch

My mother was going through a medical workup that ended in a lung cancer diagnosis. I read medical images for a living. I know what a workup is, and I know the words that can come at the end of one. Knowing did not protect me. Somewhere in that process I felt like someone had punched me in the stomach.

The question that followed was blunt. What is the point of working this hard when all the money in the world cannot buy her health back? Why had I spent so many hours at work and made so little time for the most important people in my world?

Then came a clarity I had never felt about my own life. My mother's time is finite. I did not want to spend it at work.

Death stopped being a remote possibility or an intellectual thought. It was at the doorstep, and I could not ignore it.

A word for the punch

Buddhist teacher Thanissaro Bhikkhu calls saṃvega hard to translate, because it holds at least three feelings at once: shock and dismay at how we normally live, a chastening sense of how complacently we have lived, and an anxious urgency to find a way out. In the traditional story, it is what young Prince Siddhartha felt the first time he saw aging, illness, and death, the moment that set him on the path to becoming the Buddha.

My teacher distilled it into two words I have not been able to shake: urgency and effort. The feeling brings the urgency. What you do with it is the effort.

Saṃvega also has a partner. Thanissaro describes pasāda as a clear, calm confidence that a way forward exists, and he says it keeps saṃvega from sliding into despair. You will see why that partner matters.

One word, three feelings, one direction. Saṃvega bundles shock, a chastened look at our own complacency, and urgency. Pasāda turns that urgency into steady, resolute effort. Adapted from Thanissaro Bhikkhu's essay on saṃvega and pasāda.

The four goads

The Buddha had a vivid image for how differently this jolt reaches people. In a short teaching called the Patoda Sutta, or The Goad-stick, he compares people to fine horses. One horse springs into motion at the mere shadow of the trainer's stick. Another moves when the stick touches its coat, another when it pricks the hide, and the last only when it reaches the bone.

People work the same way. Some feel stirred just hearing that someone in a distant town is suffering or has died. Some need to see it with their own eyes. Some move only when it happens to their own family, and some only when it happens to them. And in every case the teaching describes the same next step: stirred, the person becomes resolute. Urgency, then effort.

How close does suffering have to come before we move? The four goads from the Patoda Sutta (Aṅguttara Nikāya 4.113). No ring is shameful. The teaching calls every one of these horses a thoroughbred, because each of them eventually responds.

I would love to tell you I was a shadow horse. I was not. I have spent years reading the scans of strangers facing exactly this. It took my own mother.

Why this reminder finally stuck

Reminders of death surround us. The news. Obituaries. For me, the studies of patients I read every day. Most of it slides right off. So why did this one land?

Psychology offers a surprisingly precise answer. In a series of experiments, Philip Cozzolino and colleagues compared two ways of thinking about death. A brief, abstract reminder of mortality made strongly materialistic participants greedier. A specific, personal reflection did the opposite: when the same kind of participants imagined their own death in detail and looked back over their lives, they made more generous, intrinsically motivated choices. Later experiments found that this kind of specific reflection helped people pull conflicting parts of their identity together into a more coherent sense of who they are.

Death as an idea

A brief, abstract reminder that we all die someday.

Materialistic people held on tighter.

Death at the doorstep

A specific, personal, detailed reflection on a life ending.

The same people chose more generously.

That matches what I lived. Death as an intellectual possibility changed nothing about my calendar. Death at the doorstep of someone I love changed everything.

When time feels short, we choose people

Stanford psychologist Laura Carstensen built a whole theory around this shift. According to socioemotional selectivity theory, when time feels open-ended we prioritize learning, exploring, and building for later. When time feels limited, emotionally meaningful goals take over. Crucially, the shift depends on how much time we feel we have, not on age alone. Younger people shift too when something makes time feel short.

The pandemic tested this. At its peak in 2020, people of every age preferred spending time with emotionally close partners. Once vaccines arrived, the usual age differences came back. A shared brush with mortality temporarily gave everyone an older person's priorities.

Your sense of remaining time steers what you reach for. Illustration of socioemotional selectivity theory (Carstensen 1999; Jiang and Carstensen 2023). It shows direction, not measured data.

One honest caution. A shorter time horizon does not automatically make life better. In a study following 16,694 middle-aged and older adults in China, feeling that time is limited went along with more depressive symptoms. Urgency alone can curdle into dread. That is exactly why the Buddhist tradition never leaves saṃvega on its own. It pairs the jolt with pasāda, the confidence that there is something useful to do next.

The misalignment I had been living with

Here is the uncomfortable part. Like many people, I spent more waking hours at work than with my family, and I had good reasons. Income. Responsibilities. Colleagues and patients who needed me.

Medicine makes those reasons louder. In a national survey of 7,643 US physicians, 45.2% reported at least one symptom of burnout in 2023, and only 42.2% felt satisfied with how work and life fit together. After accounting for other factors, physicians had 1.82 times the odds of burnout of other US workers.

Physicians reporting burnout Physicians satisfied with work-life integration

A decade of strain, in two lines. Burnout spiked and satisfaction with work-life integration dropped in 2021, then both returned to roughly their earlier levels. Numbers from the national surveys reported by Shanafelt and colleagues (2025). Survey years are unevenly spaced.

For years I felt nervous about asking for less time at work. I wanted the timing to be good for my team. Then saṃvega handed me a truth I could not unsee: it is never good timing. Staffing will always feel tight. Someone will always need coverage. If I waited for the moment that worked for my job, I would keep waiting until the moment no longer mattered.

Urgency: the fearless ask

So I went part-time. What surprised me most was not the decision. It was the feeling behind it. The ask came from a place of fearlessness. I stopped living for my work, and I started expecting my work to fit around my family and my needs.

We give that control away quietly. We want to be team players. We don't want to ruffle feathers. We want to fit into the system and be good employees. Those are real virtues. But pushed far enough, they flip on us: the system gets first claim on our hours, and the people we love get whatever is left.

The evidence says those people are not a luxury.

50%

Higher likelihood of survival for people with stronger social relationships

Meta-analysis, 148 studies, 308,849 people (Holt-Lunstad 2010)

6,271

Adults in four countries: those who spent money to buy themselves time reported greater life satisfaction

Surveys plus a field experiment (Whillans 2017)

1.82×

The odds of burnout for US physicians compared with other US workers

National survey, 2023 (Shanafelt 2025)

In that time study, a field experiment went a step further: working adults felt happier after spending money on something that saved them time than after buying a material thing. Time, not stuff, was the scarce resource. We all compete for the same finite hours, and we rarely need as much as we think we need.

I know cutting back at work is a privilege many people do not have. Your effort does not need to look like mine. It might mean a protected family dinner, a weekly call with a parent, or a vacation day you actually take. The size of the change matters less than the fact that it happens.

Effort: catch the feeling before it fades

Saṃvega does not stay. The shock softens. We get busy, rationalize, slip back into the old rhythm, and the clarity quietly leaves the room. It is so common to go numb that it almost feels like the default.

The research agrees. When psychologists pooled 47 randomized experiments that changed people's intentions, a medium-to-large jump in intention produced only a small-to-medium change in what people actually did. Feeling urgent and acting urgently are two different things.

The intention gap. Wanting to change moved roughly twice as far as behavior did. Webb and Sheeran (2006), meta-analysis of 47 experiments.

And letting the feeling fade has a cost that grows. A classic review found that regrets about things we did sting most in the short run, but regrets about things we never did dominate in the long run. Later studies found that our most enduring regrets come from failing to become our ideal selves, the people we hoped to be, rather than failing at our duties. We fix neglected obligations quickly. We leave neglected hopes unresolved for years.

What happens after the jolt is a choice. A conceptual map drawing on the regret research (Gilovich and Medvec 1995; Davidai and Gilovich 2018) and the pairing of saṃvega with pasāda. It is not a chart of measured outcomes.

The harder path is not comfortable. Seeing the gap between what you value and how you spend your days makes most people squirm, and closing it can require profound changes. But the discomfort carries information. Researchers who study posttraumatic growth describe the changes people often report after hard experiences: a deeper appreciation of life, closer relationships, new possibilities, personal strength, and spiritual change. Growth does not erase the pain. It grows alongside it, if we let the feeling do its work.

When the goad touches you

1. Name it. Saying "this is saṃvega" turns a vague ache into something you can work with.
2. Stay with the discomfort. Resist the urge to numb it, scroll past it, or explain it away. Be wide open to feeling it.
3. Make it specific. Write down where your hours actually go and who matters most. Specific, personal reflection is what changed people's choices in the experiments; vague reminders did not.
4. Act while it still stings. Pick one concrete step and take it this week. The gap between intention and action widens with every day you wait.
5. Find your pasāda. Look for the clarity that says a way forward exists, so urgency becomes resolve instead of dread. Talk it through with someone you trust.
6. Revisit it on purpose. Thanissaro notes that the tradition asks people to reflect daily on aging, illness, separation, and death. You do not have to wait for a diagnosis to feel the shadow of the stick.

What I keep coming back to

The horse image stays with me. I did not move at the shadow of the stick. I moved when it reached someone I love. I am grateful I moved at all, and grateful for every ordinary afternoon with my mother that the move gave back to me.

If something in your chest tightened while you read this, that might be the shadow. You do not have to wait for the bone.

---

How this piece was built

The spine of this post comes from two Buddhist sources: Thanissaro Bhikkhu's essay Affirming the Truths of the Heart on saṃvega and pasāda, and the Patoda Sutta. Four research frameworks shaped how I read my own experience: socioemotional selectivity theory, the contrast between abstract mortality reminders and specific death reflection, the intention-behavior gap, and posttraumatic growth.

AI disclosure. I recorded a spoken brain dump of my experience and what saṃvega meant to me. Claude (Anthropic) wrote this draft from that recording. Claude organized my thoughts into a story, made the piece cohesive and easy to read, searched PubMed, Consensus, and the web for the research and Buddhist sources cited here, checked each number against the source abstracts, and designed all of the figures and illustrations. The experience, the insights, and the decisions described here are mine. I reviewed the draft and verified the claims and citations before publishing.

References

1. Thanissaro Bhikkhu. Affirming the truths of the heart: the Buddhist teachings on saṃvega and pasāda. Access to Insight. 1997. accesstoinsight.org
2. Patoda Sutta: The goad-stick (AN 4.113). Translated by Thanissaro Bhikkhu. dhammatalks.org
3. Cozzolino PJ, Staples AD, Meyers LS, Samboceti J. Greed, death, and values: from terror management to transcendence management theory. Pers Soc Psychol Bull. 2004;30(3):278-292. doi:10.1177/0146167203260716
4. Blackie LER, Cozzolino PJ, Sedikides C. Specific and individuated death reflection fosters identity integration. PLoS One. 2016;11(5):e0154873. doi:10.1371/journal.pone.0154873
5. Carstensen LL, Isaacowitz DM, Charles ST. Taking time seriously: a theory of socioemotional selectivity. Am Psychol. 1999;54(3):165-181. doi:10.1037/0003-066X.54.3.165
6. Jiang L, Carstensen LL. COVID-19 reduced age differences in social motivation. Front Psychol. 2023;13:1075814. doi:10.3389/fpsyg.2022.1075814
7. Zhang Y, Wei Z, Liu Y, Yu L. Future time perspective and well-being: the pitfalls of ecological fallacy and social relationship scoring. J Gerontol B Psychol Sci Soc Sci. 2025;80(12). doi:10.1093/geronb/gbaf170
8. Shanafelt TD, West CP, Sinsky C, et al. Changes in burnout and satisfaction with work-life integration in physicians and the general US working population between 2011 and 2023. Mayo Clin Proc. 2025;100(7):1142-1158. doi:10.1016/j.mayocp.2024.11.031
9. Holt-Lunstad J, Smith TB, Layton JB. Social relationships and mortality risk: a meta-analytic review. PLoS Med. 2010;7(7):e1000316. doi:10.1371/journal.pmed.1000316
10. Whillans AV, Dunn EW, Smeets P, Bekkers R, Norton MI. Buying time promotes happiness. Proc Natl Acad Sci U S A. 2017;114(32):8523-8527. doi:10.1073/pnas.1706541114
11. Webb TL, Sheeran P. Does changing behavioral intentions engender behavior change? A meta-analysis of the experimental evidence. Psychol Bull. 2006;132(2):249-268. doi:10.1037/0033-2909.132.2.249
12. Gilovich T, Medvec VH. The experience of regret: what, when, and why. Psychol Rev. 1995;102(2):379-395. doi:10.1037/0033-295X.102.2.379
13. Davidai S, Gilovich T. The ideal road not taken: the self-discrepancies involved in people's most enduring regrets. Emotion. 2018;18(3):439-452. doi:10.1037/emo0000326
14. Tedeschi RG, Calhoun LG. The Posttraumatic Growth Inventory: measuring the positive legacy of trauma. J Trauma Stress. 1996;9(3):455-471. doi:10.1007/BF02103658

Palliative Care Is Not Hospice. I Had to Learn That Twice.

The short version

• Palliative care is not hospice. It is specialist care for the symptoms and stress of a serious illness, given alongside treatment meant to cure or control the disease. You can receive it on day one, during chemotherapy, for years.
• A landmark trial changed the standard of care. Patients with newly diagnosed metastatic lung cancer who saw palliative care early reported better quality of life, had fewer depressive symptoms, received less aggressive treatment at the end of life, and lived longer.
• Guidelines now say to refer early. Oncology clinicians should refer people with advanced cancer to palliative care teams early in the disease course, alongside active cancer treatment.
• Most people have never heard of it. An estimated 71% of US adults had never heard the term. Worldwide, only about 14% of people who need it receive it.
• It is not only for cancer. Most adults who need palliative care have heart disease, lung disease, kidney failure, dementia, or another chronic illness.
• You can ask for it yourself. Skip to what to say to your doctor.

A question I did not think to ask changed my mother's life more than any scan I have ever read. It came from an AI tool, and it was five words long: have you considered palliative care?

I want to tell you about two moments, fifteen years apart, that taught me the same lesson. I needed both of them, which tells you something about how hard the lesson is for doctors to learn.

The first moment: a hospice night shift

I trained in urology before I switched to radiology. In the gap between the two, I moonlighted at a hospice and palliative care center. I took the shifts for practical reasons. What happened there rearranged how I think about medicine.

Up to that point my training had one shape. Find the disease early. Intervene. Chase the abnormal lab. Stay on top of the plan. I spent my intern and resident years in a low hum of anxiety, tracking numbers, terrified of missing something. It was all directed at the illness. I am not sure I ever asked a patient what a good day would look like for them.

At the hospice, none of that applied. Nobody was waiting on a result. Nobody was deciding whether to intervene. Every conversation started somewhere else: are you in pain, can you sleep, what is making this day hard, what do you want to be able to do. The whole apparatus was pointed at the person instead of the pathology.

I had spent years treating the disease the patient had. That was the first time I watched a team treat the patient who had the disease.

I carried that with me into radiology and mostly filed it under "formative experience." Then it came back.

The second moment: my mother's cough

Before anyone said the word cancer, my mother had a cough.

Not a polite cough. A relentless one that ran her days and then took her nights. The coughing brought on severe tension headaches. The headaches and the cough together destroyed her sleep. She was exhausted, in pain, and shrinking out of her own life, and all of it was happening before a single treatment decision had been made. Eventually that cough, along with other symptoms, led to a diagnosis of lung cancer.

I used to think of a cough as a clue. A finding that points toward a diagnosis. It took watching my mother to understand that for the person coughing, it is not a clue. It is the whole day.

The research says she was far from alone. In a study of 202 patients with lung cancer, between 57% and 67% had a cough, depending on which validated instrument was used. About 15% reported that coughing often or always disrupted their sleep, and half felt their cough was bad enough to need treatment in its own right. Cough was not associated with cancer stage or cell type. It is not a marker of how advanced the disease is. It is simply a symptom that makes life worse, and it goes underaddressed.

The question I did not know to ask

I am a physician, and I still found the diagnosis disorienting. Lung cancer is not my field. I was reading pathology reports as a daughter, not a radiologist.

So I used AI to prepare. I worked through OpenEvidence to understand what her specific pathology meant, which treatments were on the table, and what I should raise when we met the oncology team. I wanted to walk into that room able to ask real questions instead of nodding.

Among the things it surfaced was a referral to palliative care, upfront, at diagnosis.

My honest first reaction was a flinch. Palliative care sat in my mind where it sits in most people's minds: at the end, after the treatments stop working, next to the word hospice. I went looking for why the recommendation existed, and found a trial I should have known about already.

The trial that moved palliative care to the front of the line

In 2010, researchers randomized 151 patients with newly diagnosed metastatic non-small-cell lung cancer to one of two arms. One group received standard oncology care. The other received the same standard oncology care plus a palliative care team from the start. Same chemotherapy decisions, same oncologists. The only variable was an extra team focused on symptoms, coping, and what mattered to the patient.

The results, published in the New England Journal of Medicine, were not what most people expect.

Standard oncology care alone Plus early palliative care

One trial, 151 patients, metastatic non-small-cell lung cancer (Temel 2010). Compare the two bars within a panel, never across panels, since each panel uses its own scale. Every difference shown reached statistical significance, though the quality-of-life gap is modest in absolute terms: 6.5 points on a 136-point scale. This was a single-center study, and the survival finding in particular has not held up consistently in later pooled analyses. More on that below.

Less aggressive treatment at the end, and yet a longer median survival. Better mood. Better quality of life. Adding a team whose entire job was comfort did not trade away time. In this trial it came with more of it.

That finding reshaped practice. Fourteen years later, the 2024 ASCO guideline update puts it plainly: clinicians should refer patients with advanced solid tumors and blood cancers to specialized interdisciplinary palliative care teams beginning early in the disease course, alongside active treatment of the cancer. Not after. Alongside.

The picture in most people's heads is the old one

Here is the shift, drawn out. The top row is what almost everyone pictures when they hear the word. The bottom row is what the evidence supports and what guidelines now recommend.

The difference is the word "alongside." Palliative care is not what happens when treatment stops. It is a second team working in parallel with the first, from diagnosis onward. Hospice is a specific kind of palliative care for the last months of life. It is one part of the picture, not the whole of it.

The gap between those two rows is not a technicality. It is the reason people say no to something that would have helped them.

71%

Of US adults reported they had never heard of palliative care

Nationally representative survey, 3,445 adults (Trivedi 2019)

14%

Of people worldwide who need palliative care actually receive it

World Health Organization

34%

Of adults needing palliative care have cancer. Most have heart, lung, kidney or neurologic disease.

World Health Organization

What actually happened when we went

We had excellent oncologists. I want to be precise about this, because what follows is not a complaint. They were deep subspecialists doing genuinely cutting-edge work, and they were exactly who I wanted deciding what to do about the tumor.

The palliative care visit was a different kind of appointment entirely.

Nobody opened by talking about the cancer. They asked about the cough. They asked about the headaches. They asked what was happening at two in the morning when she could not stop coughing long enough to fall asleep. They asked what she used to do in a normal week that she had stopped doing.

Then they treated those things. Directly, as problems worth solving on their own terms.

The tension headaches improved. The cough at night improved. She started sleeping. And here is the part I keep turning over: she ended up with a better quality of life than she had in the months before the diagnosis, when the cough was running unchecked and nobody had named it as a target. Nothing about that came from shrinking a tumor. All of it came from treating symptoms as if they mattered.

It made a tremendous difference for her, and for all of us around her. I had read about this. I had even lived it once, at a hospice, fifteen years earlier. I still did not see it coming.

Now the part where I read the evidence critically

I am a radiologist. I look at data for a living and I am suspicious of a single glowing trial, including one that confirms something I now believe. So here is the honest state of the evidence, including where it is weaker than the enthusiasm suggests.

Quality of life and symptoms: this holds up. A meta-analysis in JAMA pooled 43 randomized trials covering 12,731 patients and found that palliative care was associated with meaningful improvements in quality of life and symptom burden at one to three months, plus consistent gains in advance care planning, patient and caregiver satisfaction, and lower health care use. When the authors restricted the analysis to the five trials at lowest risk of bias, the quality-of-life benefit shrank but stayed significant, while the symptom-burden benefit lost significance. A Cochrane review of seven trials in 1,614 people with advanced cancer reached a similar conclusion: real improvements in quality of life and symptom intensity, small in size, rated low certainty.

Survival: genuinely unsettled. The 2010 trial found longer median survival. A later trial comparing early versus delayed palliative care found one-year survival of 63% versus 48%, favoring the early group. But the JAMA meta-analysis found no significant association between palliative care and survival across all trials, and Cochrane rated the survival evidence very low certainty. My read: the quality-of-life case is settled and the longevity case is a plausible bonus that nobody should promise you. The reason to go is not that it might buy you months. The reason to go is that it will very likely make the months better.

It works beyond cancer. In a randomized trial of 150 patients with advanced heart failure, adding an interdisciplinary palliative care team to usual cardiology care produced clinically significant improvements in quality of life, depression, anxiety, and spiritual well-being at six months. Hospitalization and mortality did not change. Quality of life did.

Depression: mixed. The 2010 lung cancer trial found substantially fewer depressive symptoms. The Cochrane pooled analysis of five trials did not find a significant difference. Both things are in the literature and I am not going to pick the flattering one.

Why most people still do not get it

If the evidence is this consistent, the obvious question is why palliative care remains something families stumble into rather than something offered by default. Three reasons, all fixable.

The name. Most people have never heard the term, and many who have heard it think it means giving up. Awareness is lower in rural areas and lower among Hispanic respondents regardless of geography, which makes this an access problem, not just a vocabulary problem.

The workforce. There are not enough palliative care clinicians to see every eligible patient every month, which is the schedule most of the landmark trials used. This is stated openly as a barrier in the trial literature.

Distance and logistics. Monthly specialist visits are a real burden on a sick person and a working family.

The good news is that researchers went after the last two directly, and the results are encouraging. A trial of 1,250 patients across 22 cancer centers found that delivering early palliative care by secure video visits produced equivalent quality of life to delivering it in person. A separate trial of 507 patients tested a stepped model, where visits happen at key transition points rather than monthly, and patients step up to more intensive contact if their quality-of-life score drops. That model roughly halved the number of visits without giving up the quality-of-life benefit. Worth noting honestly: the stepped group spent fewer days in hospice, which the authors flagged rather than buried.

Between video delivery and stepped scheduling, the "we do not have the capacity" objection is losing its force.

If someone you love has a serious illness

This is the practical part. You do not have to wait to be offered palliative care. In most systems you can ask for it, and asking works.

What to say, and what to ask

• Ask directly: "Can we get a palliative care consult alongside treatment?" The word "alongside" does a lot of work. It signals you are not asking to stop treatment.
• Name the symptoms, not the diagnosis. Cough, pain, nausea, breathlessness, insomnia, anxiety, appetite. These are the targets. List the ones wrecking the day.
• Say what a good day would look like. Palliative teams build the plan around this, and most of us never get asked.
• Ask if it can be virtual. The evidence says video visits work as well as in person for this.
• Ask what the team covers: symptom management, mood, coordination between specialists, help for the caregiver, and planning conversations. It is usually a physician, nurse, social worker, and chaplain, not one person.
• Ask about coverage. Palliative care is a medical subspecialty billed like any other. Medicare, Medicaid, and most private plans cover it.
• If nobody has an answer, the Center to Advance Palliative Care runs a public provider directory searchable by ZIP code and setting.

One more note, from the daughter rather than the doctor. Bring someone to the appointment, and make sure the person who is sick gets asked the questions directly. My mother does not speak English as her first language. In every room we sat in, the risk was that the conversation would happen over her head and around her, between clinicians and the family member who could keep up. The palliative team was the one that consistently aimed the questions at her.

What I keep coming back to

The thing that took me two decades to absorb is that treating the disease and treating the person are not the same project, and doing one well does not accomplish the other. Our oncologists were excellent at the first. Nothing about their excellence produced a night of sleep.

Both projects are real. Both need someone assigned to them. The evidence says running them in parallel from day one makes people feel better, and may do more than that.

I learned this once in a hospice fifteen years ago and then let it fade into a nice story about my training. It took my mother's cough, and a question from a machine, to make me actually use it.

---

How this piece was built

I started with a brain dump. I talked out the whole story, unstructured and unedited: the hospice moonlighting, my mother's cough, the AI query, the palliative care visit, what changed. Then I handed that raw transcript to Claude (Anthropic) and worked with it to turn the material into this piece.

AI disclosure. The experience, the argument, and the point of view are mine. Claude searched PubMed, Consensus, and the WHO for the peer-reviewed evidence cited here and verified every trial number against the source abstract, produced both figures, structured the narrative from my spoken brain dump, and helped me tighten the draft. I directed the framing, supplied the experience, and reviewed every claim, number, and citation before publishing. Separately, I used OpenEvidence during my mother's care to prepare questions for her oncology team, which is how the palliative care referral came up in the first place.

References

1. Temel JS, et al. Early palliative care for patients with metastatic non-small-cell lung cancer. N Engl J Med. 2010;363(8):733-742. doi:10.1056/NEJMoa1000678
2. Sanders JJ, et al. Palliative care for patients with cancer: ASCO guideline update. J Clin Oncol. 2024;42(19):2336-2357. doi:10.1200/JCO.24.00542
3. Kavalieratos D, et al. Association between palliative care and patient and caregiver outcomes: a systematic review and meta-analysis. JAMA. 2016;316(20):2104-2114. doi:10.1001/jama.2016.16840
4. Haun MW, et al. Early palliative care for adults with advanced cancer. Cochrane Database Syst Rev. 2017;6(6):CD011129. doi:10.1002/14651858.CD011129.pub2
5. Bakitas MA, et al. Early versus delayed initiation of concurrent palliative oncology care: patient outcomes in the ENABLE III randomized controlled trial. J Clin Oncol. 2015;33(13):1438-1445. doi:10.1200/JCO.2014.58.6362
6. Zimmermann C, et al. Early palliative care for patients with advanced cancer: a cluster-randomised controlled trial. Lancet. 2014;383(9930):1721-1730. doi:10.1016/S0140-6736(13)62416-2
7. Rogers JG, et al. Palliative care in heart failure: the PAL-HF randomized, controlled clinical trial. J Am Coll Cardiol. 2017;70(3):331-341. doi:10.1016/j.jacc.2017.05.030
8. Greer JA, et al. Telehealth vs in-person early palliative care for patients with advanced lung cancer: a multisite randomized clinical trial. JAMA. 2024;332(14):1153-1164. doi:10.1001/jama.2024.13964
9. Temel JS, et al. Stepped palliative care for patients with advanced lung cancer: a randomized clinical trial. JAMA. 2024;332(6):471-481. doi:10.1001/jama.2024.10398
10. Harle A, et al. A cross sectional study to determine the prevalence of cough and its impact in patients with lung cancer: a patient unmet need. BMC Cancer. 2020;20(1):9. doi:10.1186/s12885-019-6451-1
11. Trivedi N, et al. Awareness of palliative care among a nationally representative sample of U.S. adults. J Palliat Med. 2019;22(12):1578-1582. doi:10.1089/jpm.2018.0656
12. Langan E, et al. Comparing palliative care knowledge in metropolitan and nonmetropolitan areas of the United States: results from a national survey. J Palliat Med. 2021;24(12):1833-1839. doi:10.1089/jpm.2021.0114
13. World Health Organization. Palliative care fact sheet. 2020. who.int

Peer-reviewed sources were located through PubMed and Consensus. Nothing in this post describes anyone's protected health information beyond what my family has chosen to share.

Moving Is the New Standing: Why I Traded My Chair for a Treadmill

Life as a radiologist means living in front of a screen. A regular day means hours at a reading station, scrolling through CT and MRI. A weekend on call can mean twelve straight hours in the chair. Sitting seems harmless because everyone does it. It isn't.

The wake-up call

In my mid-thirties, I considered myself pretty athletic. I had run a marathon during fellowship and stayed active. Then I spent two weeks writing a grant, leaning into my computer in the same seated position, hour after hour, day after day. Those two weeks ended with low back pain so sudden and severe that it landed me in the hospital.

I had a standing desk available at the time. I sat anyway, because the chair always offers the path of least resistance. I have lived with low back pain and some disc disease in my lower spine ever since. A marathon a year earlier didn't protect me from what two weeks in a chair did.

I'm far from the only one. When researchers surveyed 123 radiologists, 38% reported a work-related musculoskeletal injury, and low back discomfort topped the list (Rodrigues, J Digit Imaging 2014). And it isn't just us. The average US adult now sits about 6.4 hours a day, nearly an hour more than in 2007 (Yang, JAMA 2019).

What sitting does to a body

My turning point came when I read Deskbound: Standing Up to a Sitting World by physical therapist Kelly Starrett, written with Juliet Starrett and Glen Cordoza. Its core premise: your body adapts to whatever positions you hold most. Sit most of the day, and your body gets very good at sitting. Starrett describes the lower back flattening and the upper back and shoulders rounding forward. It reminds me of playing guitar. Press the strings long enough and your fingertips grow calluses. Your body protects itself by taking the shape of your habits.

As a radiologist, I can show you part of this on imaging. When researchers compared sitting and standing X-rays of the lumbar spine in 109 patients with low back pain, the lumbar curve averaged 49 degrees standing and only 34 degrees sitting (Lord, Spine 1997).

Figure 1. Same people, same spines, different posture. Curves are schematic; the angles are the study's measured averages. Source: Lord et al., Spine 1997.

Your discs care about movement too. In a classic experiment, researchers placed a pressure sensor inside a volunteer's lumbar disc and recorded a full day of ordinary life. They concluded that constantly changing position helps move fluid, and with it nutrition, into the disc (Wilke, Spine 1999). The same study also challenged a number you may have heard: that sitting loads your discs far more than standing. In their measurements, relaxed standing and unsupported sitting produced similar pressures, with sitting slightly lower. That study followed one person, so I treat it as a clue rather than a verdict. But both findings point the same way: position changes matter more than any one "correct" posture.

A radiologist's caveat. How permanent these adaptations become, and how directly posture causes back pain, remains unsettled. A review of 41 systematic reviews linked sitting, standing, and awkward postures to low back pain but found no consensus that any single posture causes it (Swain, J Biomech 2019). The better-supported lesson is that holding any one position for hours is the problem.

A drop at a time

Even a drop of water will eventually fill a bucket. The hours in a chair add up quietly over a career. The largest analysis of this question pooled data from more than one million adults followed for up to 18 years (Ekelund, Lancet 2016). People who sat more than 8 hours a day and moved the least had a 59% higher risk of dying during follow-up than people who sat less than 4 hours and moved the most. Now the hopeful part: people who sat more than 8 hours a day but also got roughly 60 to 75 minutes of moderate activity daily showed no significant increase in risk.

Figure 2. Four cells from the harmonised meta-analysis. These are observational data with self-reported sitting and activity, so they show association, not proof of cause. Source: Ekelund et al., Lancet 2016.

Moving is the new standing

After reading the book, I switched to standing. It took time, but standing became my default. Then, after a few hours on my feet, a familiar ache would creep back into my lower back. Sitting down for a while eased it. I had simply traded one static posture for another.

Research explains why. In lab studies, people with no history of back pain stood for longer than 42 minutes, and about half of them (528 of 1,070) developed low back pain (Khoshroo, Sci Rep 2023). A separate meta-analysis of desk work found that prolonged standing caused no less low back pain than prolonged sitting (De Carvalho, Work 2020). Standing isn't a cure. Moving is the point.

So I added a small under-desk treadmill. At about 1 mile per hour, the standing ache doesn't show up, my hands stay steady on the mouse and keyboard, and my body keeps moving. You don't need a workout. You need motion.

Can a radiologist really read while walking? One study tested exactly that. Three radiologists read 55 lung cancer screening CTs twice, once seated and once walking on a treadmill workstation. They found about as many nodules walking as seated (no statistically significant difference), made consistent follow-up recommendations, and finished each exam faster while walking (Johnson, JACR 2019). It was a small study, but a reassuring one.

Expect a learning curve. In a lab study of first-time treadmill users with no practice, typing, mouse clicking, and math problem solving slipped by 6% to 11%, while attention, processing speed, and reading comprehension held steady (John, J Phys Act Health 2009). Start slow and give yourself a few weeks.

Your metabolism notices the difference, too. Walking and working at about 1.1 mph burned roughly 120 more calories per hour than seated work in a study of 15 office workers with obesity (Levine and Miller, Br J Sports Med 2007). And in a randomized crossover trial of 19 adults with overweight or obesity, a 2-minute light walk every 20 minutes lowered the blood sugar rise after a meal by about 25% and the insulin rise by about 24%, compared with sitting straight through (Dunstan, Diabetes Care 2012).

Figure 3. Both walking conditions differed significantly from uninterrupted sitting (P < 0.01). Small trial, one day per condition, adults aged 45 to 65. Source: Dunstan et al., Diabetes Care 2012.

The psychological toll

This past weekend I was on call. Both days ran twelve hours, nonstop and busy. I skipped my run and my walks and barely moved. By the end, I felt terrible, and not only in my back. I felt flat, foggy, and low.

Movement matters for the mind as much as the body. We often credit endorphins for that lift. Whatever the mechanism, the data are striking. Across 15 studies and more than 191,000 adults, people who got just half the recommended amount of activity had an 18% lower risk of depression than inactive people, and those who met the recommendation had a 25% lower risk (Pearce, JAMA Psychiatry 2022). In one workplace study, sit-stand desks cut sitting by 66 minutes a day, reduced upper back and neck pain by 54%, and improved mood. When researchers took the desks away, most of those gains disappeared within two weeks (Pronk, Prev Chronic Dis 2012).

That finding explains my call weekend. You can't bank movement. It works more like a daily dose.

Start small

Don't wait for a hospital visit to change your habits. Here is what works for me:

• Make standing the starting position. Set up your desk standing, so sitting becomes a deliberate choice instead of the default.
• Move before it hurts. At the first twinge from standing, walk for a while or sit for a stretch. Rotate through positions all day.
• Walk slowly. About 1 mph keeps your hands steady and your body moving.
• Break up long stretches. A 2-minute walk every 20 to 30 minutes, or between cases, adds up over a shift.
• Protect movement on your hardest days. Those are the days you need it most.
• Ask for the equipment. Sit-stand desks cut workplace sitting by about 100 minutes a day in the short term and about an hour a day at 3 to 12 months (Shrestha, Cochrane 2018). That gives you a solid case to request one.

My next goal is more time on the treadmill, adding minutes slowly the same way I once added standing time. It took two weeks of stillness to put me in the hospital. Small daily choices are what keep me out of it.

This post shares my own experience and my reading of the research. It is not medical advice. If you have new, severe, or worsening back pain, please see your clinician.

References

1. Starrett K, Starrett J, Cordoza G. Deskbound: Standing Up to a Sitting World. 2016.
2. Rodrigues JCL, et al. Musculoskeletal symptoms amongst clinical radiologists and the implications of reporting environment ergonomics. J Digit Imaging. 2014;27(2):255-261. doi:10.1007/s10278-013-9642-3
3. Yang L, et al. Trends in sedentary behavior among the US population, 2001-2016. JAMA. 2019;321(16):1587-1597. doi:10.1001/jama.2019.3636
4. Lord MJ, et al. Lumbar lordosis: effects of sitting and standing. Spine. 1997;22(21):2571-2574. doi:10.1097/00007632-199711010-00020
5. Wilke HJ, et al. New in vivo measurements of pressures in the intervertebral disc in daily life. Spine. 1999;24(8):755-762. doi:10.1097/00007632-199904150-00005
6. Swain CTV, et al. No consensus on causality of spine postures or physical exposure and low back pain. J Biomech. 2019;102:109312. doi:10.1016/j.jbiomech.2019.08.006
7. Ekelund U, et al. Does physical activity attenuate, or even eliminate, the detrimental association of sitting time with mortality? Lancet. 2016;388(10051):1302-1310. doi:10.1016/S0140-6736(16)30370-1
8. Khoshroo F, et al. Distinctive characteristics of prolonged standing low back pain developers. Sci Rep. 2023;13(1):6392. doi:10.1038/s41598-023-33590-5
9. De Carvalho D, et al. Does objectively measured prolonged standing for desk work result in lower ratings of perceived low back pain than sitting? Work. 2020;67(2):431-440. doi:10.3233/WOR-203292
10. Johnson CR, et al. Effect of dynamic workstation use on radiologist detection of pulmonary nodules on CT. J Am Coll Radiol. 2019;16(4 Pt A):451-457. doi:10.1016/j.jacr.2018.10.017
11. John D, et al. Effect of using a treadmill workstation on performance of simulated office work tasks. J Phys Act Health. 2009;6(5):617-624. doi:10.1123/jpah.6.5.617
12. Levine JA, Miller JM. The energy expenditure of using a "walk-and-work" desk for office workers with obesity. Br J Sports Med. 2007;41(9):558-561. doi:10.1136/bjsm.2006.032755
13. Dunstan DW, et al. Breaking up prolonged sitting reduces postprandial glucose and insulin responses. Diabetes Care. 2012;35(5):976-983. doi:10.2337/dc11-1931
14. Pearce M, et al. Association between physical activity and risk of depression. JAMA Psychiatry. 2022;79(6):550-559. doi:10.1001/jamapsychiatry.2022.0609
15. Pronk NP, et al. Reducing occupational sitting time and improving worker health: the Take-a-Stand Project, 2011. Prev Chronic Dis. 2012;9:E154. doi:10.5888/pcd9.110323
16. Shrestha N, et al. Workplace interventions for reducing sitting at work. Cochrane Database Syst Rev. 2018;12:CD010912. doi:10.1002/14651858.CD010912.pub5

How I made this post. I dictated my story and asked Claude, Anthropic's AI assistant, to co-draft it. Claude searched PubMed for the studies cited here, fact-checked and merged two earlier drafts into this version, and built all three figures from numbers reported in those papers. I reviewed and edited the text, checked the figures and citations against the original studies, and decided what to keep. The story, the back, and the opinions are mine.

The Good Trials Are Better Than I Expected. The Failure Modes Are Worse.

The 30-second version

• When an AI handed radiologists the wrong answer, very experienced readers went from scoring 82.3% of mammograms correctly to 45.5%. The least experienced fell to 19.8%. Experience helped. It did not protect.
• And yet the best evidence is genuinely good: in a randomized trial of 105,934 women, AI-supported screening found more cancers with 44% less reading, with no rise in interval cancers.
• Lab performance does not transfer. The same class of tool that dazzles in a trial hit 35% sensitivity in a real primary-care population.
• So the answer is not "does AI work." It is does this model still work here, this month, which is a monitoring problem, and radiology has quietly started building the boring infrastructure to solve it.

The most important number I found while researching this series is not a sensitivity or an area under a curve. It is what happened to expert radiologists when the machine was confidently wrong.

This is the second of three posts. The first argued that the fear of AI eliminating jobs is aimed at the wrong target, because health care cannot staff the work it already has. That argument has a hole in it, and I want to put my finger in it before going any further: a staffing crisis is a reason to want a tool. It is not evidence that the tool works.

So this post is the evidence. All of it, including the parts I wish were different.

The short version is that the good trials are better than I expected and the failure modes are worse. Both of those things are true at once, and any version of this conversation that gives you only one of them is selling something.

What the good evidence actually shows

The strongest data we have comes from breast screening, because that is where somebody finally did the randomized trial.

The MASAI trial in Sweden randomized 105,934 women to either AI-supported screening or standard double reading by two radiologists. The AI triaged which exams needed a second reader and flagged suspicious findings. The final results, published in The Lancet in 2026, reported the primary outcome: the interval cancer rate, meaning cancers that surface between screening rounds because the screen missed them. That rate was 1.55 per 1,000 with AI versus 1.76 without, which met the trial's bar for non-inferiority. Sensitivity was higher with AI, 80.5% versus 73.8%. Specificity was identical at 98.5%.

Two radiologists, no AI AI-supported reading

One randomized trial, 105,934 women, Sweden. More cancers found, higher sensitivity, same specificity, and 44% less reading. The three panels use different scales, so only the within-panel comparison is meaningful. Detection and workload figures from the 2025 Lancet Digital Health report; sensitivity from the 2026 Lancet primary analysis of the same trial.

The workload number is the one I keep coming back to. AI-supported screening required 61,248 screen readings where standard double reading required 109,692. That is a 44% reduction in reading, with more cancers found and no significant increase in false positives.

Germany replicated the direction at enormous scale. The PRAIM study followed 463,094 women screened by 119 radiologists across 12 sites and found a cancer detection rate of 6.7 per 1,000 with AI support versus 5.7 without, a 17.6% relative increase, with a recall rate that was slightly lower rather than higher. One important caveat: PRAIM was observational and the radiologists chose for themselves whether to use the AI, so the groups were not randomly assigned and the comparison is weaker than MASAI's.

Notice what actually improved. Not the radiologist's eye. The radiologist's throughput, and the number of second reads that never needed a human at all.

The place this matters most is not Scottsdale

Everything above happened in wealthy countries with organized screening programs and plenty of radiologists. The larger prize is somewhere else entirely.

In Bangladesh, researchers ran 23,954 chest X-rays from three tuberculosis screening centers past five commercial AI algorithms and a panel of three registered radiologists. All five algorithms significantly outperformed the radiologists, and all five cut the number of confirmatory molecular tests needed by about half while holding sensitivity above 90%. The authors also reported that every algorithm performed worse in people over 60 and in people with a history of TB, which is exactly the kind of subgroup detail that gets dropped when these results are summarized.

In China, a study across 7 county-level and 32 township-level facilities reviewed 93,319 patients, of whom 273 had bacteriologically confirmed pulmonary TB. The AI flagged 83.9% of those confirmed cases; the radiologists reading at the time caught 25.6%. The AI's positive predictive value was much worse, 1.7% against 10.3%, meaning far more false alarms. But used as a triage filter with human review of flagged images, it cut the radiologist workload by 85.5% without missing any case the radiologists had found on their own.

A separate validation on more than one million chest X-rays reported an AUC of 98.51% and a false negative rate slightly better than the radiologists', with the potential to auto-report up to 80% of normal studies.

These are the numbers that make me hopeful, and they have almost nothing to do with whether AI is better than me. They are about places where there is no radiologist to be better than. That is where "raising all boats" stops being a slogan.

Four findings that should keep us honest

I do not want to write a brochure. Here is the evidence that cuts the other way, and some of it is genuinely alarming.

OR 1.20

Higher odds of burnout among radiologists who used AI frequently, with a dose-response by frequency of use

6,726 radiologists, 1,143 hospitals (Liu 2024)

−6.0pts

Drop in adenoma detection when endoscopists went back to working without AI, after months of using it

1,443 colonoscopies, 4 centers (Budzyń 2025)

35%

Sensitivity of a commercial chest X-ray AI in a real-world, low-prevalence screening population

3,047 radiographs, 2 primary care centers (Kim 2023)

Automation bias is worse than I expected. In a prospective experiment, 27 radiologists read mammograms with a purported AI system that was deliberately wrong on 12 of 40 cases. Among the most experienced readers, the share of correctly assigned BI-RADS categories fell from 82.3% to 45.5% when the AI suggested the wrong category. Among the least experienced, it fell from 79.7% to 19.8%. Experience helped. It did not protect.

AI suggested the correct category AI suggested the wrong one

Experience buys you some protection. Not much. All six bars are zero-based on one shared scale, so every length is directly comparable. The three groups perform almost identically when the machine is right. The gap opens only when it is wrong. This was a prospective experiment with a purported AI system, not a deployed product, which makes it a clean measure of the effect and not a claim about any commercial tool. Dratsch et al., Radiology 2023.

Deskilling may be real. Four Polish endoscopy centers compared adenoma detection during unassisted colonoscopy in the three months before AI was introduced and the three months after. The rate fell from 28.4% to 22.4%, an absolute drop of 6 percentage points. This was a retrospective before-and-after comparison, not a randomized one, so seasonality, case mix, and staffing changes are all live alternative explanations. But the effect size is large enough that dismissing it would be motivated reasoning.

Lab performance does not transfer automatically. A commercial chest X-ray AI validated against CT findings in 3,047 consecutive radiographs from two primary healthcare centers, where the prevalence of significant disease was 2.2%, achieved a sensitivity of 35.3% and an AUROC of 0.648. The authors' conclusion is the sentence I would put on a poster in every department: regulatory approval and experimental performance may not translate to real practice, and the mismatch tends to be worst exactly where the need is greatest.

AI did not make radiologists less burned out. A survey of 6,726 radiologists across 1,143 Chinese hospitals found that frequent AI users had higher odds of burnout than non-users, with an adjusted odds ratio of 1.20 and a dose-response relationship with frequency of use, driven mostly by emotional exhaustion. It was worst among radiologists with high workloads. This is cross-sectional, so causality could run either way. But it points at something that matches my experience: if you speed up one task and leave the volume expectation untouched, you have not reduced anyone's suffering. You have just changed what they do all day and asked for more of it.

Equity does not happen by itself

The version of this future I want is one where a woman in a rural county gets her MRI read this week instead of in March. But the technology does not deliver that on its own, and there is a well-documented case showing exactly how it fails.

A commercial algorithm used across US health systems to identify patients needing extra care was found to be substantially biased against Black patients: at any given risk score, Black patients were considerably sicker than white patients. The mechanism was not malice or a bad training set in the usual sense. The algorithm predicted health care costs as a proxy for illness, and because less money has historically been spent on Black patients, the proxy encoded the disparity. Correcting it would have raised the share of Black patients flagged for additional help from 17.7% to 46.5%.

That is a design decision, not an accident of the math. Somebody chose a convenient outcome variable. The same choice is available to every group building an imaging model right now, and it will be made well or badly depending on who is in the room.

My own specialty has actually built something

Here is the part of this story I did not expect to be writing, and the part I am proudest of. While the broader AI conversation has been arguing about whether guardrails are even possible, radiology quietly went and built some.

In June 2024 the American College of Radiology launched ARCH-AI, the ACR Recognized Center for Healthcare-AI, described as the first national quality assurance program for AI in medical imaging. To earn the designation, a practice attests to a specific set of things: that it has an interdisciplinary AI governance group, that it keeps a documented inventory of every algorithm it runs, that it has a deliberate process for reviewing and selecting those algorithms, that it does acceptance testing before deployment, that it monitors performance afterward, and that it manages any models it built itself.

None of that is glamorous. All of it is exactly what was missing in the failure modes above.

ARCH-AI is deliberately a stepping stone. The ACR leadership behind it have written openly that it exists as a precursor to a formal accreditation program, on the same model the College has used since radiation oncology in 1966 and mammography in 1987, with council approval anticipated around spring 2027. Their stated reason for building it is the same observation this whole post keeps circling: real-world AI performance often differs from what premarket testing showed. That sentence is in the ACR's own road map paper. It is not a criticism from outside the field.

The piece I find genuinely impressive is the second one. In November 2024 the ACR launched Assess-AI, a registry inside the National Radiology Data Registry that monitors how deployed imaging AI is actually performing, in real practices, over time. Participating sites send de-identified algorithm outputs, report text, and study metadata. The registry extracts surrogate labels from the radiology reports, computes concordance between what the algorithm said and what the radiologist ultimately said, and returns it as dashboards. Sites can compare themselves against national benchmarks and against peers matched on facility type, region, trauma level, and urban versus rural. They can drill into discordant cases and look at whether the disagreements cluster by demographic or technical factor. It currently covers intracranial hemorrhage, pulmonary embolism, pneumothorax, large-vessel occlusion, bone age, and cervical spine fracture.

Sit with what that is for a moment. It is post-market surveillance for algorithms, built by the specialty that uses them, that measures whether a model still works in your department, on your scanners, with your patients, after the vendor demo is over. Model drift is not hypothetical; departments change their protocols, equipment, and case mix constantly, and performance moves with them. Assess-AI is the mechanism for noticing.

In 2026 the ACR and SIIM also approved a formal practice parameter covering tool selection, predeployment evaluation, ongoing monitoring, and privacy, and the program has begun expanding internationally, with the University Hospital of Bern named its first site outside the United States.

Radiology did not wait to be regulated. It built the registry, wrote the parameter, and put a badge on the wall. I would like the rest of the AI industry to notice that this was possible.

I want to be measured about it. ARCH-AI is attestation, not audit: a practice affirms it is doing these things rather than being inspected. Assess-AI depends on voluntary participation and on surrogate labels pulled from report text by a language model, which is a reasonable approximation of truth and not truth itself. Neither program stops a department from buying a bad algorithm. What they do is make it much harder to buy one and never find out.

Before your department buys an imaging AI, ask these

ARCH-AI covers the institutional layer. These are the clinical questions underneath it.

• What population was it validated on, and what was the disease prevalence in that population compared to ours?
• What are the reported subgroup results by age, sex, race, body habitus, and scanner vendor? If there are none, that is the answer.
• Is it a triage tool, a second reader, or a concurrent reader? Each one fails differently and each one needs a different workflow.
• What happens to the reading list when it is wrong, and how would we ever find out that it was?
• Does the volume expectation change when the tool goes live? If throughput goes up and staffing does not, we have bought a burnout accelerator.
• Are we submitting to Assess-AI, and if not, what is our alternative plan for catching drift?
• How do we preserve the skills of residents and junior attendings who will train alongside it?

What this post does not tell you

Two posts in, I have argued that the work is moving rather than vanishing, and that the technology is real but conditional: good in the trial, fragile in the field, safe only with a loop and a registry behind it.

Both of those are arguments about what medicine should do. They assume medicine gets to decide the pace.

I no longer think that is true. Several hundred million people a week are already asking these systems health questions, and a growing number have connected their own medical records to them. The knowledge asymmetry that defined the exam room for a century is closing from the patient's side, and nobody asked us.

That is the last post.

---

How this piece was built

Every trial number here was checked against the source abstract rather than a summary of it, and where a study is observational, small, or before-and-after rather than randomized, I have said so in the same sentence as the finding rather than in a footnote. Two figures in this post carry warnings about how to read them; please read them.

AI disclosure. The argument and the point of view are mine. I worked with Claude (Anthropic) as a research and drafting partner: it searched PubMed and Consensus, verified each trial's numbers against the primary source, produced the two figures, and helped organize the draft. I reviewed every claim and citation before publishing.

References

1. Hernström V, et al. Screening performance and characteristics of breast cancer detected in the Mammography Screening with Artificial Intelligence trial (MASAI): a randomised, controlled, parallel-group, non-inferiority, single-blinded, screening accuracy study. Lancet Digit Health. 2025;7(3):e175-e183. doi:10.1016/S2589-7500(24)00267-X
2. Hernström V, et al. Interval cancers and screening outcomes in the MASAI trial. Lancet. 2026;407(10430):505-514. doi:10.1016/S0140-6736(25)02464-X
3. Lång K, et al. Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): a clinical safety analysis. Lancet Oncol. 2023;24(8):936-944. doi:10.1016/S1470-2045(23)00298-X
4. Eisemann N, et al. Nationwide real-world implementation of AI for cancer detection in population-based mammography screening. Nat Med. 2025;31(3):917-924. doi:10.1038/s41591-024-03408-6
5. Dratsch T, et al. Automation bias in mammography: the impact of artificial intelligence BI-RADS suggestions on reader performance. Radiology. 2023;307(4):e222176. doi:10.1148/radiol.222176
6. Budzyń K, et al. Endoscopist deskilling risk after exposure to artificial intelligence in colonoscopy: a multicentre, observational study. Lancet Gastroenterol Hepatol. 2025;10(10):896-903. doi:10.1016/S2468-1253(25)00133-5
7. Kim JH, et al. Clinical validation of a deep learning-based software for lung nodule detection in chest radiographs in a health screening population. Eur Radiol. 2023;33(11):7823-7833. doi:10.1007/s00330-023-09761-3
8. Liu Y, et al. Artificial intelligence use and burnout among radiologists in China. JAMA Netw Open. 2024;7(12):e2448714. doi:10.1001/jamanetworkopen.2024.48714
9. Qin ZZ, et al. Tuberculosis detection from chest x-rays for triaging in a high tuberculosis-burden setting: an evaluation of five artificial intelligence algorithms. Lancet Digit Health. 2021;3(9):e543-e554. doi:10.1016/S2589-7500(21)00116-3
10. Jiang Y, et al. Effectiveness of computer-aided detection for active pulmonary tuberculosis screening in resource-limited settings. J Med Internet Res. 2025;27:e69109. doi:10.2196/69109
11. Munjal P, et al. Assessing the reliability of an AI-based chest radiograph interpretation system on over one million radiographs. NPJ Digit Med. 2025;8(1):318. doi:10.1038/s41746-025-01693-0
12. Obermeyer Z, et al. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366(6464):447-453. doi:10.1126/science.aax2342
13. Larson DB, et al. A road map for the ACR Recognized Center for Healthcare-AI. J Am Coll Radiol. 2025;22(5):586-592. doi:10.1016/j.jacr.2025.02.008
14. Coombs LP, et al. The ACR Assess-AI registry: national performance monitoring of clinical AI. J Am Coll Radiol. 2026;23(9):1557-1566. doi:10.1016/j.jacr.2026.04.024

Peer-reviewed sources were located through PubMed and Consensus. Nothing in this post describes any individual patient or protected health information, and nothing here represents the position of my employer.