待翻譯：The AI Health Agent That Will Know Me for Life

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William Tunstall-Pedoe Aug 25, 2026 Before giving my AI agent access to all my health data, I gave it a constitution. Its highest-priority goal is to maximise the probability of me reaching my 90th birthday in excellent physical and mental health. Beneath that sits present-day health and wellbeing, aesthetics and athletic performance. In that order. The ordering matters and goals matter. Health is not a single value to maximise. Losing weight may improve one set of risks but compromising muscle or energy. Training harder may improve athletic fitness but increase injury risk. A drug that improves sleep may leave me less mentally sharp. An AI agent that does not understand what I am trying to achieve can optimise the wrong thing. I am trying to build something far more ambitious than a health chatbot: a universal personal health agent that can accumulate evidence across my lifetime, work alongside my doctors, learn from experiments and data, intervene constructively in my day-to-day life and become more effective as both AI and medical science improves. The full version I imagine is still ahead of me, but something recognisably like it is remarkably now possible. The models are early, the integrations are imperfect and much of the operating discipline is still being iterated. But enough of the technology exists that I have started building it for one human being: me. What I am building is less a full health application than the substrate for one: a fully patient-controlled medical health record organised around a person rather than an institution, preserving not only conclusions but where they came from and how certain they are. The agent reading that record will be replaced many times. The record itself must grow and last. Figure 1. The constitution of my personal health AI agent. Before it can reason about my health, the agent needs to know what “better” means and how competing objectives should be prioritised... Medicine remembers appointments. I want AI that remembers a life. Today’s health information is extraordinarily fragmented. A hospital remembers appointments. A primary-care record remembers prescriptions and diagnoses. A laboratory remembers test results. My Oura ring remembers sleep, activity and physiology. A Withings scale remembers weight. My Garmin watch remembers my runs. I sometimes remember symptoms, meals, conversations with doctors, why I agreed to a treatment and the questions I forgot to ask. Some of these memories are wrong and most eventually disappear. Even when the data exists, it typically lives in separate systems designed for different purposes. The organising principle of a medical record is often the institution and the encounter, rather than the continuing life of the patient. I want to turn that fragmented record inside out. The organising principle should be the person. The system I am building should be able to absorb essentially any kind of information that does, or might, matter to health, and connect it over time. That means breadth across all domains and data types: blood tests, imaging, genetics, medications, symptoms, sleep, diet, exercise, research questions, mood, consultations, photographs and whatever new classes of evidence become useful later. This is not just a long-term medical record. It is a patient-controlled, continuously updated model of a life, designed from the outset to be usable by both humans and AI agents. It should know what happened, why it happened, what we currently believe, how confident we are, what remains unresolved and what should happen next. All pointing back directly to the evidence. An intelligent health agent therefore needs more than just access to data. It needs explicit goals, rules about what counts as evidence, a disciplined way to represent uncertainty and a detailed model of the person it serves that is continuously kept up-to-date. A living health model, not a filing cabinet My current implementation is built around a health workspace stored in Notion and an AI agent I’ve called Aster. My workspace contains original clinical documents, structured measurements, treatments, observations, nutrition, alcohol and caffeine events, health hypotheses, experiments, specialist interactions, open health questions and a continuously updated synthesis of my health status. The distinction between evidence and interpretation is crucial. Original documents are preserved. Structured facts should point back to their sources. Measurements are distinguished from estimates and contain error bars. Hypotheses are distinguished from established findings. Current treatments are distinguished from historical treatments. Uncertainty is not something to be ignored or tidied away. It is part of the data. Figure 2. Memory with provenance. The agent is not supposed to only remember conclusions. It must preserve where they came from, what kind of claim they are and enable them to be recomputed later.... This matters because the current model is temporary. The health history is not. I expect the AI models I use in two, five or ten years time to be dramatically better than those I use today. Medical science will also improve. I therefore require the underlying evidence to survive the particular intelligence that first built and interpreted it. My current AI agent will be replaced many times. The history it is building should last my lifetime. AI as a new kind of health sensor Wearables have already transformed one part of this problem. Without doing anything other than occasionally recharging my devices, I can collect huge quantities of data about sleep, heart rate, HRV, exercise, movement and many other aspects of physiology. However, until recently one enormous plank of my health data was missing: everything that I consume. Nutrition data was completely missing. Food logging applications work tolerably when somebody eats a packaged product with a barcode or carefully weighs every ingredient at home. Real life is dramatically messier. I eat in restaurants and get food delivered. I share dishes. I taste somebody else’s starter. A plate arrives with an unknown amount of oil. I eat half of something and take the rest home. I eat the leftovers the following day or throw them away. A menu omits ingredients. I photograph a dish before eating it and then remember later that I left most of the sauce. Drinks arrive at different times. Representing all of this accurately by hand is so tedious that almost nobody will do it consistently for long. However, the AI agent I built moves this from a data-entry chore into something that can happen almost as a by-product of ordinary life. On a typical day the active effort is only a few minutes, mostly taking photographs, brief text or voice notes and occasionally correcting the agent. I can photograph a menu and ask what I should eat. The agent knows what I have already had that day, which nutritional goals currently matter and even my preferences. I can photograph my starter and say that the spicy tuna is made with vegetables rather than rice. It updates its estimate. I can photograph my main course and say it is mine while the side dishes are being shared. Rather than inventing a portion, it can wait until I tell it roughly how much I ate. The transformational capability is not recognising a sea bass. It is understanding the complex, real-world semantics of an actual meal. AI is becoming a kind of semantic sensor: it can turn messy human events into analysable, structured data without requiring human life itself to become machine-readable. The estimates deliberately preserve the uncertainty. A restaurant meal might have a central calorie estimate with a wide plausible range. That is much more useful than pretending that a photograph can reveal an exact number. Crucially, the raw evidence survives the estimate. The agent retain the photographs, original descriptions, timestamps, assumptions and corrections. A future vision model may estimate the same meal better. It may know more about the restaurant, ingredients, portion sizes or my own patterns. Historical data can therefore be revisited and improved rather than permanently frozen at the quality of today’s model. If the raw evidence survives, the historical health record can retrospectively improve as the interpreting intelligence improves. Figure 3. AI as a semantic sensor. From restaurant meal to personal health data. A menu becomes a personalised recommendation; natural-language corrections update the estimate; shared portions remain unresolved until the agent has enough information. Much of this was designed by me in 2020. I included a worked example called Chea in the broad technical disclosure underlying UnlikelyAI‘s patent filings. One patent figure shows a user sending food photographs while Chea asks whether the dressing had been added to the salad. The same disclosure described uncertainty about food composition and portion size, combines nutrition with sleep and other health data, possible dietary intolerance inferred from accumulated data, and caffeine at bedtime compared with wearable-derived sleep quality. This was implementable then. What has changed is the friction and the development effort: semantic machinery that would once have required significant bespoke engineering can now be implemented by a general model inside an ordinary conversation and built by me as a personal project. Figure 4. The same interaction, six years apart. Chea was a worked example in a broad technical disclosure underlying UnlikelyAI’s patent filings; Aster is the personal AI system in daily use in 2026. From data to personal understanding Collecting data is only useful if it improves what I understand and what I do. A recent example is sleep. I had a long-standing form in insomnia where I would often wake in the early hours and sometimes remain awake for a long time. In June, I started taking daridorexant, a medicine from a newer class of sleep drugs. My Oura ring had already collected the before-and-after data and these metrics had been automatically imported into my health workspace. But the interesting question was not just whether my generic sleep score changed. It was whether the specific problem I was taking the medicine for had improved. Aster compared adjacent periods before and after treatment and examined prolonged stretches of being awake beginning after 1am. The reduction was striking. It also found something I had not appreciated: my total sleep had improved much less than the reduction in night-time wakefulness because my bedtime had gradually crept later. The drug appeared to be fixing the problem I cared about, while a behavioural change, probably encouraged by sleeping better, was giving back some of the gain. That was immediately useful. The analysis was not a clinical trial and Oura is not a polysomnograph but it quantified something important that matched my subjective experience and gave useful evidence to take to my sleep consultant. Figure 5. Long night-time awakenings before and after daridorexant. This is an uncontrolled before-and-after comparison using Oura estimates, but it focuses on the symptom that mattered to me. The episode also exposed one of the dangers of 2026 AI. An earlier analysis focused on the wrong metric, constructed a plausible but invented story around it and attributed a complaint to me that I never made. A personal research programme The more interesting questions go beyond just analysing my own measurements. I carry the LRRK2 G2019S genetic variant, which substantially increases my lifetime risk of Parkinson’s disease. I do not have Parkinson’s disease, but reducing the risk of developing it is one of my most important long-term health objectives sitting alongside other major age-correlated diseases such as heart disease and cancer. That creates questions that are highly consequential to me and unusually personali [truncated for AI cost control]