Dr. Jay Luthar described the flaw in modern medicine in a single sentence, and once you hear it, you can’t unhear it. “The traditional medical model to this point has used population studies to find what the average person responds to and then fits an individual to an average,” he said. “Whereas we know that every individual is not an average, but very individual.”
Tell me if this sounds familiar. Your doctor treats you with drugs validated on a population you don’t belong to. The trial reported an average response. You are not the average. Nobody is. Some patients in that trial got better, some got nothing, a few got worse, and the number that reached your chart was the mean of all three.
The research bears this out uncomfortably well. Randomized trials are designed and powered to estimate average treatment effects, not what will happen to you specifically. For most treatments, only a small share of patients responds the way the “average” patient did. Clinicians have been guessing from that average for a century and calling it ‘evidence-based’.
Luthar, who is on faculty at Harvard Medical School and founded Lutanen Health, wants to invert it. Look at the patient in front of you “almost like an n-of-1 research study.” That phrase isn’t a metaphor he made up. N-of-1 trials are a real design, and the Evidence-Based Medicine Working Group of the AMA has held that they sit at the top of the evidence hierarchy for decisions about an individual patient. The catch, which is real, is that the statistical methods for aggregating them aren’t standardized, and they carry almost no weight in guidelines or regulatory decisions.
Dr. Julie Chen, chief medical officer at Radence and formerly CMO at Human Longevity, reframes the same shift as a question. Traditional medicine asks, in her words, “are you sick yet.” Radence asks whether you are “changing in a not-so-great direction.” If the answer is yes, you’re still inside the window where prevention means something.
The machinery underneath that question got real in the last two years. Luthar is most excited about proteomics, and the numbers explain why. We carry tens of thousands of protein signatures. Researchers at Stanford measured more than 7,000 plasma proteins across 60,542 people and built models estimating the biological age of over 40 cell types. Roughly 20% to 25% of people showed accelerated aging in a single cell type. The associations are not subtle: accelerated organ aging carried 20% to 50% higher mortality risk, accelerated heart aging came with a 250% higher heart failure risk, and people with extremely aged skeletal myocytes had a 12.7-fold higher risk of developing ALS.
Two patients can look identical on paper, Luthar said, same age, same gender, same diagnosis, and have “totally different underlying physiology, totally different reason for even having the same disease.” The proteome sees the difference. The intake form never will.
Chen’s version runs through imaging and time. Not a single snapshot but a trend line: organ volumetrics, brain atrophy, liver iron, visceral fat, muscle composition, tracked against your own baseline rather than a reference range built from strangers. “Clinicians are as good as the data that we get,” she said, “so it’s not about getting massive amounts of data but getting the right data.”
She also flagged the part that should interest anyone writing checks in this category. Interventions can now be audited per person. A treatment “supposed to help them” can be checked against whether it actually is, in that specific body, compared to cohorts that resemble them instead of a general population “where there could be people that have nothing, no characteristics like you.”
So that’s the promise, and the market seems to think it is real. The precision medicine market ran between $110 billion and $119 billion in 2025 depending on whose model you trust, with forecasts in the $400 billion range within a decade. Multi-omics is the small, fast piece: about $3.1 billion in 2025, projected past $12 billion by 2035.
Measuring more of a healthy person is not the same as helping them, and medicine has learned this the hard way. Whole-body MRI screening for asymptomatic people confirms cancer in roughly 1.1% to1.5% of scans while turning up a much larger pile of incidental findings. The American College of Radiology doesn’t recommend it for people without symptoms, risk factors, or relevant family history, and JAMA has published against elective whole-body scans on overdiagnosis grounds. Patients get follow-up tests. Some get surgery for things that would never have hurt them. All of them get to carry the knowledge that something is in there.
The genomics layer has its own asterisk. Luthar cited the MGH/MIT polygenic risk score for cardiovascular stratification, and polygenic scores do work as risk enhancers. They also inherit a structural bias: because most genome-wide association studies were run on people of European ancestry, the scores predict less well for everyone else. In one Broad Institute validation cohort, there weren’t enough cardiovascular events among non-European ancestry groups to produce stable estimates. A tool that personalizes better for some patients than others is not a small footnote in a story about ending the average patient.
To their credit, neither doctor oversold it. Chen was blunt that AI is “not at a point where clinical decision-making or judgment can be the end-all, be-all yet,” and that “humility in medicine” is part of the job. Her own operation runs a team of MD-PhDs whose function is reading the thousands of studies published daily to sort what is evidence-based from what is merely interesting. That team exists because the field generates more claims than it does proof.
Luthar’s framing of the near term is the honest one. Focus on early detection and lifestyle, and “help people live healthily up until some of these more revolutionary treatments can come down the pipeline.” Translation: the personalization is real, the therapeutics are not there yet, and the job right now is staying alive and in good repair long enough to use them.
