I came across a new study this week that really got me thinking. And the paper shined a new light on the value of large language models (LLMs) in medicine and even beyond. Researchers took a stripped-down version of GPT—a model with only about two million parameters—and trained it on individual medical diagnoses like hypertension and diabetes. Each ICD-10 code became a token, like a word in the sentence of a prompt, and each person’s medical history became a story unfolding over time.
For a little context, GPT-4 and GPT-5 are believed to have hundreds of billions to trillions of parameters, making them hundreds of thousands of times larger than this small model. And yet, this tiny system was able to predict the “next word” in a person’s health story, including the next diagnosis, the next complication, and, with uncanny precision, even the timing of death. For me, that was a full stop. Let’s dig in.
Seeing the Arc of Illness
It’s interesting to note that clinicians already think this way. A 50-year-old with hypertension might not alarm a doctor, but add diabetes and chronic kidney disease, and the physician starts to see the arc of possible futures that may include heart failure, dialysis, and even premature death. What this new model does is formalize and scale that intuition. It has seen hundreds of thousands of similar “patients” and knows, statistically, how their stories usually unfold and when.
This isn’t just predicting one outcome; it’s simulating a trajectory and forecasting which complications are likely to develop and how quickly they might appear. Think of it as if you were taking a snapshot of your current health and running it forward in time to see your clinical future.
Predicting the Day You Die
One result was especially striking to me. The model could correctly distinguish who would die and who would live 97 percent of the time. That’s astonishingly accurate, especially given that the model was working with nothing more than a handful of diagnoses, age, and basic lifestyle factors.
The Grammar of Disease
Now let’s talk about the power of diagnostic “language” in the context of a large “language” model. It seems as if AI is learning what might be called the grammar of disease. Each diagnosis is a “word,” each medical record is a “sentence,” and our lives are written in sequences of these tokens. Add hypertension and hyperlipidemia, and the model can already sketch a likely next chapter. Add diabetes or kidney disease, and the story becomes sharper, the ending more predictable. Each “word” adds more context and increases the statistical probability.
When AI Reads More Than Our Health
So, let’s push on this a bit. What does it mean, psychologically, ethically, and even practically, when a machine can read the story of our health and tell us what comes next or even how it might end? Do we really want to know our likely “final chapter,” even if we can’t change it? Or could this knowledge be used to rewrite the story and to intervene earlier, shift the trajectory, and add new chapters we never expected?
Interestingly, this doesn’t stop at medicine. If a model can learn the grammar of disease, what about the grammar of psychology that looks at the sequence of experiences—captured in a diagnosis or word—that lead to depression or burnout? What about the grammar of relationships and the patterns that predict divorce or reconciliation? LLMs, trained on millions of human stories, already contain traces of these patterns.
So, this is where it gets both thrilling and unnerving for me. These models don’t just predict words; they predict the future shape of human lives, at least in a probabilistic or statistical sense. They hint at the possibility that every aspect of our existence, including our health, our choices, even our heartbreaks and heartbeats, has a statistical signature that can be read and projected into the future.
Which leaves us with a very human question. How much of this do we actually want to know? There’s power in foresight, but also a kind of burden. Perhaps the challenge isn’t just building models that can read our stories but in deciding when and how we want them to tell us what comes next.