Fifty Patients

Evidence strong enough that no one, including FDA, can object

I. The phone call

Late in 2015, someone inside FDA picked up the phone and called Pfizer.

The request was simple. The oncology division wanted whatever evidence the company possessed on crizotinib in patients with ROS1-positive non-small cell lung cancer. There was no large randomized trial waiting in the wings, no thousand-patient database still being cleaned, no elaborate statistical exercise. Pfizer sent what it had.

It amounted to fifty patients.

The evidence came from a single-arm study led by Alice Shaw at Massachusetts General Hospital, published in the New England Journal of Medicine the previous year. Independent review found an objective response rate of 66%, slightly lower than the investigators had reported. The National Comprehensive Cancer Network had already incorporated crizotinib into its treatment guidelines for ROS1-positive disease. By the time the supplemental application reached FDA, the clinical community had, in effect, already rendered its verdict.

In March 2016, FDA approved crizotinib for metastatic ROS1-positive non-small cell lung cancer. Priority review had compressed the timeline to roughly six months. The entire evidentiary package could have fit inside a modest summary table.

Those of us working at FDA felt something difficult to admit publicly. We had not led the field. We had arrived after it.

What even seasoned industry veterans tend to misunderstand about FDA, and about most regulatory authorities of its kind, is what such agencies actually do. They do not manufacture clinical conviction. They do not, in the first instance, decide what constitutes evidence or what counts as benefit. Those judgments are made elsewhere: in laboratories and clinics, in academic departments and professional societies, in trials and meta-analyses and the slow accumulation of shared experience. By the time a regulator acts, the community has usually already reached its conclusion. What FDA does, at its best, is ratify it.

II. The last reader

FDA occupies a singular place in modern medicine, but not quite the one people imagine.

It is tempting to think of the agency as the author of clinical standards, the institution that decides what constitutes evidence and, by extension, what becomes accepted practice. Its decisions undoubtedly shape medicine. Patients gain or lose access to therapies because of them. Companies rise and fall on them. Yet FDA’s confidence almost never emerges in isolation. More often, it crystallizes only after the scientific community has spent years generating, debating, and refining the evidence.

In that sense, FDA is less the author than the final reader.

The pattern repeats so consistently that, once recognized, it becomes difficult to ignore. Companion diagnostics, biomarker-driven approvals, surrogate endpoints, molecular classifications, imaging criteria: nearly all entered regulatory practice only after clinicians, investigators, statisticians, and professional societies had begun converging on a shared understanding of what the evidence meant.

RECIST is perhaps the clearest example.

The Response Evaluation Criteria in Solid Tumors was not written by FDA. It emerged in 2000 from an international collaboration among the European Organisation for Research and Treatment of Cancer, the US National Cancer Institute, and the National Cancer Institute of Canada. FDA adopted RECIST because the clinical trial community had adopted it and, imperfect as it was, nothing better existed.

Twenty-five years later, RECIST still anchors nearly every regulatory decision based on tumor-based endpoints in solid tumors, its limitations well documented and widely acknowledged. In a meta-analysis I conducted at FDA involving roughly 13,677 paired radiologic assessments, radiologists disagreed on response category about 30% of the time, and over 40% for hard-to-measure tumors. Investigators know this. Sponsors know it. FDA knows it. And yet RECIST persists, because no alternative has accumulated enough evidence to command broader consensus.

Regulators do not abandon standards simply because they are imperfect. They abandon them when the community offers something demonstrably better.

III. The lipstick and the scan

When I took the results of that meta-analysis to one of the senior officials at the FDA, he read the numbers without much surprise. Then he looked up.

“You know, Sean, I never liked RECIST.”

He said it the way you say something you have said to yourself, quietly, for years.

“Do you know what I trust the most?”

“What?”

“The lipstick sign.”

He told me about a patient he had once treated, years earlier. A woman with metastatic colorectal cancer had arrived for treatment frail, cachectic, and visibly declining. After several cycles of chemotherapy she returned transformed. She had regained weight. Color had returned to her face. She walked into clinic wearing bright red lipstick.

“I really didn’t need to see the scan,” he said. “I knew the treatment was working. We would continue therapy.”

That kind of observation still carries real evidentiary weight in everyday practice. Almost no oncologists outside of clinical trials use RECIST. Radiology reports in the metastatic setting tend toward a softer, descriptive vernacular: this lesion is smaller, that one stable, correlate clinically. Clinical benefit is something experienced before it is measured.

Inside the trial world, that era has largely disappeared.

Today the scan is expected to settle the question. RECIST measurements, lesion diameters, independent central review, progression-free survival curves, objective response rates: these have become the language through which benefit is translated into evidence. The patient still matters, of course, but the patient’s improvement must now survive quantification before it can influence a regulatory decision.

FDA did not invent that transformation. The community did.

Once the community accepted RECIST-defined imaging as the common language of efficacy, the agency adopted the same language because it had become the only one everyone understood. The senior official had spent his career watching that shift. And still, when asked what he trusted, he named the lipstick.

That was the first time I realized that the FDA does not manufacture consensus. It waits for a consensus it can ratify.

IV. What FDA clearance cannot buy

None of this is confined to the drug world of the last generation. The same dynamic is now playing out around clinical AI, and playing out badly.

Nearly every ambitious clinical AI company in oncology has organized itself around FDA. Get the clearance. Attend the workshops. Cultivate the reviewers. The unstated assumption behind all of this is that FDA is the gatekeeper whose approval unlocks the market.

If the argument of the preceding sections holds, that assumption is inverted. FDA is not the gatekeeper. It is the last reader, waiting for the community to arrive at a conclusion the agency can ratify. A tool built to persuade FDA before it has persuaded anyone else is a tool built for the wrong step in the sequence.

The clearest illustration of the alternative comes not from clinical AI but from a molecular diagnostic developed a generation earlier. The specific route it took is no longer available to most software today, but the strategic logic behind it still is.

Consider Oncotype DX. Its developer, Genomic Health, was founded in 2000 and eventually acquired by Exact Sciences in 2019 for approximately $2.8 billion. The company built a 21-gene recurrence score assay for early-stage, hormone receptor-positive breast cancer, and made a deliberate choice not to seek FDA clearance. It ran the test as a laboratory-developed test out of a single CLIA-certified laboratory, and directed its evidence at oncologists and payers rather than regulators. The American Society of Clinical Oncology and NCCN incorporated the assay into their guidelines. Medicare covered it. TAILORx and RxPONDER, both prospective, established clinical utility. FDA never approved Oncotype DX. It never had to. The moat has held for more than twenty years.

That exact route is closed to most clinical AI. Software as a medical device making substantive clinical claims cannot route around FDA the way Oncotype DX did as a laboratory-developed test (LDT). The 2024 attempt by FDA to bring LDTs under device regulation was vacated in March 2025 by the Eastern District of Texas, and the agency formally rescinded the rule in September 2025. That ruling does not extend to software. SaMD is a device. It falls squarely within the Federal Food, Drug, and Cosmetic Act. The only narrow exit is the Clinical Decision Support carveout in the 21st Century Cures Act, and most substantive clinical AI cannot fit through that door, unless one is developing free software that clinicians will likely never use.

But the strategy behind Oncotype DX still applies. Strong evidence is universal currency. It compels agreement wherever it lands, from oncologists, from guideline committees, from payers, and yes, from FDA. The mistake most clinical AI companies make is not that they have prioritized FDA in the wrong order. It is that they have treated FDA as the audience whose blessing is the point of the exercise. FDA is one reader among several. Depending on the product, it may not be the most consequential. For an LDT, the agency is not the decision-maker at all. For a SaMD, it matters, but no more than a guideline committee or a payer. Build a tool that oncologists actually need. Generate the evidence to prove it. The rest, FDA included, will follow.

What most clinical AI companies do instead is pursue 510(k) clearance and hope that a demonstration of substantial equivalence to some predicate device, often something that predates the modern imaging or oncology workflow by a decade, will function as a proxy for clinical validation. It does not. Physicians do not treat 510(k) clearance as evidence. Payers do not reimburse against it. The ten-thousandth 510(k)-cleared stethoscope is fine. A 510(k)-cleared oncology AI tool making substantive claims about patient management is not fine, because the pathway was never designed to answer the question the market is actually asking.

There is a related misapprehension that keeps consulting rosters full. It is the belief that access to former FDA officials, however senior, can substitute for the evidence the market is asking for. Companies pay handsomely for such counsel, hoping that some hidden regulatory knowledge, some subtle understanding of how the agency really thinks, will unlock a path their peers have not found.

It does not exist.

Ex-FDA leaders can offer real value. They can explain established standards. They can help navigate submission logistics. They can flag procedural risks outsiders would miss. What they cannot do is dispense a formula for how to redefine a paradigm, or how to push a novel biomarker or endpoint through the agency. Not because they are withholding it. Because it is not theirs to withhold. Paradigms are not redefined at FDA. They are redefined by the community that generates the evidence. FDA, in due course, follows.

The question the market is asking is: does this tool change what I should do for my patient? That question can only be answered by prospective data. It cannot be answered by regulatory clearance of any kind, except a PMA with prospective (often randomized) data, which is the kind of evidence that will move not only the regulator but the community at large, including payers and clinicians.

V. The contract that kills

This raises an obvious question. If prospective evidence is the only real path, why do so few AI companies take it?

The answer is culture and worldview.

The implicit contract most clinical AI companies signed with their investors is a software contract. Fast iteration. Low marginal cost. Quick path to revenue. SaaS multiples. That contract is structurally incompatible with the kind of evidence generation the clinical AI products actually require. You cannot run a prospective validation study in a diagnostic imaging AI on a two-year software burn rate. The math does not work.

This is why so many companies in the space end up chasing 510(k) shortcuts and FDA relationship-building. It is not that some founders do not understand what real validation looks like. It is that they raised on a timeline that does not permit it. The shortcut is the only path available given the capital structure, and the shortcut leads to a product physicians will not adopt and payers will not reimburse.

The honest version of the pitch to investors is this. Clinical AI making substantive claims is not a software business. It is a diagnostics business with a software component. The comparables are not Epic or Veeva. The comparables are Genomic Health. Foundation Medicine, taken private by Roche in 2018 at an enterprise value of roughly $5.3 billion. Guardant Health, still public and worth several billion dollars. Each of those companies spent years pre-revenue or sub-scale, generating clinical evidence, before the market gave them the returns they eventually earned. None of them cut corners on evidence. All of them built moats many of their competitors still cannot cross.

An investor who does not understand this should not be in the round. A founder who does not raise from investors who understand this will be forced into the shortcut, and the shortcut will kill the company.

VI. The evidence is the moat

For the founders and investors who do accept the trade, the upside is enormous.

Category ownership in diagnostics generates margins and durability that most software businesses envy. Once a tool is in NCCN, once payers reimburse at scale, once the clinical evidence has been written into guidelines, the incumbent’s advantage becomes structural. It is not the algorithm. It is not the code. It is not the clearance. It is the evidence. And the evidence takes years, hundreds of millions of dollars in some cases, and prospective trials that a competitor cannot shortcut. That is the true barrier to entry, and it is one a 510(k) can never create.

There is a second-order effect that first-movers capture and fast-followers cannot. The company that runs the first prospective trial in a category also gets to help define what the endpoints in that category should be. It gets to shape the consensus FDA will eventually align with. That is not merely a business advantage. It is a form of scientific authorship, and it accrues to whoever is willing to fund it.

But scientific authorship, ultimately, is not the work of any single company. It is the work of a community. Investigators. Physicians. Statisticians. Professional societies. Guideline committees and sponsors. All converging on shared standards for what evidence should look like. Sometimes that means defending the standards we have. Often it means replacing them with better ones.

When clinical AI approaches FDA with bespoke solutions that carry no peer validation, with half-formed proposals that lack credibility among the stakeholders, the agency cannot act unilaterally to sanction them. In a way, it should not. Manufacturing consensus is not what regulators are for. That responsibility belongs to us. Building conviction strong enough, and universal enough, that the data speaks for itself is not FDA’s job.

Which brings us back to fifty patients.

Fifty patients was never the story.

Fifty patients was simply the point at which the story no longer required additional chapters.

By the time FDA approved crizotinib, the agency was not discovering the truth. It was acknowledging one the clinical community had already accepted.

Every successful regulated medical product eventually reaches that moment. The question is never whether the evidence satisfies a regulator but whether the evidence has become so persuasive that every serious reader arrives at the same conclusion. When that happens, guideline committees converge, payers follow, physicians change practice, competitors fall behind, and FDA’s decision becomes almost inevitable.

The moat is not the algorithm. It is not the regulatory clearance. It is not even the product.

The moat is the evidence.

Everything else follows from that.

And the evidence is ours to build.