The Big Y #313
AI clinical reasoning
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A recent Harvard study has shown that LLMs can outperform doctors in triaging and diagnosing emergency room patients based on the information available upon arrival to the hospital. Initial ER admission is challenging because doctors must make split-second, life-altering decisions using sparse, âmessyâ data from electronic health records and the paramedics (if you have watched The Pitt you know what I am talking about). The study specifically isolated text-based clinical reasoning to see how well the AI could process raw medical data compared to a humanâs interpretation of that same data.
The study tested OpenAIâs o1 model and human physiciansâ abilities to identify correct diagnoses at three specific stages of care: triage, evaluation, and admission. It found that the model generally outperformed human clinicians and existing diagnostic tools, and the more information available to the AI (and humans) quickly pushed the accuracy up. Initial patient triaging is critical and decisions are often reliant on low-information and incomplete data situations, where this type of AI can quickly process the information to hand off to a doctor to manage IRL patient care.
Systems integrating AI can act as a sophisticated second opinion that evaluates a patientâs medical history alongside the doctorâs initial assessment. Implementing LLMs more strategically in triaging can potentially help support overstretched emergency rooms. This isnât replacing doctors, but can be a key partner in improving patient outcomes.
The Tidbit: Earning reports came out last week and AI spending is higher than ever. Across Amazon, Microsoft, Google, and Meta the expected spending this year is $700 billion, an insane amount of money which is largely going into data centers. This spending was contributing 1.5 percentage points to the 2% GDP growth rate in Q1 2026.
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