It has been a breathless time in technology since the GPT-3 moment, and I’m not sure I have experienced greater discordance between the hype and reality than right now, at least as it relates to healthcare. To be sure, I have caught myself agape in awe at what LLMs seem capable of, but in the last year, it has become ever more clear to me what the limitations are today and how far away we are from all “white collar jobs” in healthcare going away.

Microsoft had an impressive announcement last week with The Path to Medical Super-Intelligence with its claim that its AI Diagnostic Orchestrator (MAI-DxO) correctly diagnosed up to 85% of NEJM case proceedings, a rate more than four times higher than a group of experienced physicians (~20% accuracy). While this is an interesting headline result, I think we are still far from “medical superintelligence”, and in some ways, we underestimate what human intelligence is good at it, particularly in the healthcare context.

Beyond potential issues of benchmark contamination, the data for Microsoft’s evaluation of its orchestrator agent is based on NEJM case records that are highly curated, teaching narrative summaries. Compare that to a real hospital chart: a decade of encounters scattered across medication tables, flowsheets, radiology blobs, scanned faxes, and free-text notes written in three different EHR versions. In that environment, LLMs lose track of units, invent past medical history, and offer confident plans that collapse under audit. Two Epic pilot reports—one from Children’s Hospital of Philadelphia, the other from a hospital in Belgium—show precisely this gap and shortcoming with LLMs. Both projects needed dozens of bespoke data pipelines just to assemble a usable prompt, and both catalogued hallucinations whenever a single field went missing.

The disparity is unavoidable: artificial general intelligence measured on sanitized inputs is not yet proof of medical superintelligence. The missing ingredient is not reasoning power; it is reliable, coherent context.

Messy data still beats massive models in healthcare

Transformer models process text through a fixed-size context window, and they allocate relevance by self-attention—the internal mechanism that decides which tokens to “look at” when generating the next token. GPT-3 gave us roughly two thousand tokens; GPT-4 stretches to thirty-two thousand; experimental systems boast six-figure limits. That may sound limitless, yet the engineering reality is stark: packing an entire EHR extract or a hundred-page protocol into a prompt does not guarantee an accurate answer. Empirical work—including Nelson Liu’s “lost-in-the-middle” study—shows that as the window expands, the model’s self-attention diffuses. With every additional token, attention weight is spread thinner, positional encodings drift, and the transformer’s gradient now competes with a larger field of irrelevant noise. Beyond a certain length the network begins to privilege recency and surface phrase salience, systematically overlooking material introduced many thousands of tokens earlier.

In practical terms, that means a sodium of 128 mmol/L taken yesterday and a potassium of 2.9 mmol/L drawn later that same shift can coexist in the prompt, yet the model cites only the sodium while pronouncing electrolytes ‘normal. It is not malicious; its attention budget is already diluted across thousands of tokens, leaving too little weight to align those two sparsely related facts. The same dilution bleeds into coherence: an LLM generates output one token at a time, with no true long-term state beyond the prompt it was handed. As the conversation or document grows, internal history becomes approximate. Contradictions creep in, and the model can lose track of its own earlier statements.

Starved of a decisive piece of context—or overwhelmed by too much—today’s models do what they are trained to do: they fill gaps with plausible sequences learned from Internet-scale data. Hallucination is therefore not an anomaly but a statistical default in the face of ambiguity. When that ambiguity is clinical, the stakes escalate. Fabricating an ICD-10 code or mis-assigning a trial-eligibility criterion isn’t a grammar mistake; it propagates downstream into safety events and protocol deviations.

Even state-of-the-art models fall short on domain depth. Unless they are tuned on biomedical corpora, they handle passages like “EGFR < 30 mL/min/1.73 m² at baseline” as opaque jargon, not as a hard stop for nephrotoxic therapy. Clinicians rely on long-tail vocabulary, nested negations, and implicit timelines (“no steroid in the last six weeks”) that a general-purpose language model never learned to weight correctly. When the vocabulary set is larger than the context window can hold—think ICD-10 or SNOMED lists—developers resort to partial look-ups, which in turn bias the generation toward whichever subset made it into the prompt.

Finally, there is the optimization bias introduced by reinforcement learning from human feedback. Models rewarded for sounding confident eventually prioritize tone that sounds authoritative even when confidence should be low. In an overloaded prompt with uneven coverage, the safest behavior would be to ask for clarification. The objective function, however, nudges the network to deliver a fluent answer, even if that means guessing. In production logs from the CHOP pilot you can watch the pattern: the system misreads a missing LOINC code as “value unknown” and still generates a therapeutic recommendation that passes a surface plausibility check until a human spots the inconsistency.

All of these shortcomings collide with healthcare’s data realities. An encounter-centric EHR traps labs in one schema and historical notes in another; PDFs of external reports bypass structured capture entirely. Latency pressures push architects toward caching, so the LLM often reasons on yesterday’s snapshot while the patient’s creatinine is climbing. Strict output schemas such as FHIR or USDM leave zero room for approximation, magnifying any upstream omission. The outcome is predictable: transformer scale alone cannot rescue performance when the context is fragmented, stale, or under-specified. Before “superintelligent” agents can be trusted, the raw inputs have to be re-engineered into something the model can actually parse—and refuse when it cannot.

Context engineering is the job in healthcare

Andrej Karpathy really nailed it here:

Context engineering answers one question: How do we guarantee the model sees exactly the data it needs, in a form it can digest, at the moment it’s asked to reason?

In healthcare, I believe that context engineering will require three moves to align the data to ever-more sophisticated models.

First, selective retrieval. We replace “dump the chart” with a targeted query layer. A lipid-panel request surfaces only the last three LDL, HDL, total-cholesterol observations—each with value, unit, reference range, and draw time. CHOP’s QA logs showed a near-50 percent drop in hallucinated values the moment they switched from bulk export to this precision pull.

Second, hierarchical summarisation. Small, domain-tuned models condense labs, meds, vitals, imaging, and unstructured notes into crisp abstracts. The large model reasons over those digests, not 50,000 raw tokens. Token budgets shrink, latency falls, and Liu’s “lost-in-the-middle” failure goes quiet because the middle has been compressed away.

Third, schema-aware validation—and enforced humility. Every JSON bundle travels through the same validator a human would run. Malformed output fails fast. Missing context triggers an explicit refusal.

AI agents in healthcare up the stakes for context

The next generation of clinical applications will not be chatbots that answer a single prompt and hand control back to a human. They are agents—autonomous processes that chain together retrieval, reasoning, and structured actions. A typical pipeline begins by gathering data from the EHR, continues by invoking clinical rules or statistical models, and ends by writing back orders, tasks, or alerts. Every link in that chain inherits the assumptions of the link before it, so any gap or distortion in the initial context is propagated—often magnified—through every downstream step.

Consider what must be true before an agent can issue something as simple as an early-warning alert:

• All source data required by the scoring algorithm—vital signs, laboratory values, nursing assessments—has to be present, typed, and time-stamped. Missing a single valueQuantity.unit or ingesting duplicate observations with mismatched timestamps silently corrupts the score.
• The retrieval layer must reconcile competing records. EHRs often contain overlapping vitals from bedside monitors and manual entry; the agent needs deterministic fusion logic to decide which reading is authoritative, otherwise it optimizes on the wrong baseline.
• Every intermediate calculation must preserve provenance. If the agent writes a structured CommunicationRequest back to the chart, each field should carry a pointer to its source FHIR resource, so a clinician can audit the derivation path in one click.
• Freshness guarantees matter as much as completeness. The agent must either block on new data that is still in transit (for example, a troponin that posts every sixty minutes) or explicitly tag the alert with a “last-updated” horizon. A stale snapshot that looks authoritative is more dangerous than no alert at all.

When those contracts are enforced, the agent behaves like a cautious junior resident: it refuses to proceed when context is incomplete, cites its sources, and surfaces uncertainty in plain text. When any layer is skipped—when retrieval is lossy, fusion is heuristic, or validation is lenient—the agent becomes an automated error amplifier. The resulting output can be fluent, neatly formatted, even schema-valid, yet still wrong in a way that only reveals itself once it has touched scheduling queues, nursing workflows, or medication orders.

This sensitivity to upstream fidelity is why context engineering is not a peripheral optimization but the gating factor for autonomous triage, care-gap closure, protocol digitization, and every other agentic use case to come. Retrieval contracts, freshness SLAs, schema-aware decoders, provenance tags, and calibrated uncertainty heads are the software equivalents of sterile technique; without them, scaling the “intelligence” layer merely accelerates the rate at which bad context turns into bad decisions.

Humans still have a lot to teach machines

While AI can be brilliant for some use cases, in healthcare so far, large-language models still seem like brilliant interns: tireless, fluent, occasionally dazzling—and constitutionally incapable of running the project alone. A clinician opens a chart and, in seconds, spots that an ostensibly “normal” electrolyte panel hides a potassium of 2.8 mmol/L. A protocol digitizer reviewing a 100-page oncology protocol instinctively flags that the run-in period must precede randomization, even though the document buries the detail in an appendix.

These behaviors look mundane until you watch a vanilla transformer miss every one of them. Current models do not plan hierarchically, do not wield external tools unless you bolt them on, and do not admit confusion; they generate tokens until the temperature hits zero. Until we see another major AI innovation like the transformer models themselves, healthcare needs a viable scaffolding that lets an agentic pipeline inherit the basic safety reflexes clinicians exercise every day.

That is not a defeatist conclusion; it is a roadmap. Give the model pipelines that keep the record complete, current, traceable, schema-tight, and honest about uncertainty, and its raw reasoning becomes both spectacular and safe. Skip those safeguards and even a 100-k-token window will still hallucinate a drug dose out of thin air. When those infrastructures become first-class, “superintelligence” will finally have something solid to stand on.