Microsoft Pushes Toward ‘Medical Superintelligence’ in Healthcare

Can artificial intelligence (AI) make more accurate diagnoses than doctors? Microsoft reports its AI has begun to do just that as it moves into complex healthcare decisions.

Last summer, Microsoft said its AI Diagnostic Orchestrator, known as MAI-DxO, correctly solved 85.5% of complex case records from the New England Journal of Medicine. A group of 21 experienced physicians from the United States and the United Kingdom, tested on the same cases, achieved a mean accuracy of 20%. Microsoft also said the system reached correct diagnoses at a lower cost by ordering fewer virtual diagnostic tests. The results remain under external peer review as of Tuesday (March 17) and have not yet been published in a scientific journal.

Benchmark performance and product rollout show AI is moving from administrative use to direct roles in diagnosis and care decisions.

From Documentation Tool to Diagnostic Engine

Healthcare AI once automated notes and referrals. Now, it is shifting toward clinical reasoning.

A Google Cloud report found that 44% of healthcare executives had AI agents in production as of late 2025, with organizations reallocating budgets toward systems capable of executing defined clinical decisions under human supervision. The same report found that 90% of healthcare leaders reported positive returns from generative AI, particularly in patient screening, imaging analysis and automated documentation.

Microsoft’s MAI-DxO illustrates this shift. It does not use a single model, but simulates a panel of clinicians by coordinating multiple language models. These models ask questions, order virtual tests and refine reasoning before making a diagnosis. This mirrors collaborative decision-making.

Microsoft noted the benchmark’s limits. The system used curated case records, not real-time patient interactions. Physicians in the study worked alone, without colleagues or external references, unlike typical clinical practice.

The company’s newly introduced Copilot Health extends this capability into a consumer-facing environment. The platform aggregates personal health records, wearable data from more than 50 devices and lab results from over 50,000 U.S. hospitals, synthesizing that information into personalized insights. Microsoft said Copilot Health is not intended to deliver clinical diagnoses, positioning it instead as an early step toward what it describes as “medical superintelligence.”

AI as the Front Door to Healthcare

The rise of diagnostic AI is occurring alongside a shift in how patients access care. Microsoft said its platforms, including Bing and Copilot, now handle more than 50 million health-related sessions per day, reflecting growing reliance on AI for initial medical guidance.

As PYMNTS previously reported, OpenAI has said more than 40 million people use ChatGPT daily for health-related queries, with roughly 70% of those interactions occurring outside traditional clinic hours. In underserved and rural areas, AI tools are increasingly meeting demand that existing healthcare infrastructure cannot.

This behavioral shift positions AI systems as an entry point into the healthcare system, influencing how and when patients seek care. Rather than beginning with a primary care visit, many patients now start with conversational AI, which can shape symptom interpretation, triage decisions and provider selection.

The economic implications are significant. U.S. healthcare spending is approaching 20% of gross domestic product, and Microsoft estimates that up to 25% of that spending produces little measurable improvement in patient outcomes. Systems that can reduce diagnostic uncertainty earlier in the care journey could lower costs while improving efficiency, strengthening the case for broader deployment.

Despite progress, major risks exist. Generative AI sometimes gives confident but wrong answers, a problem in clinical settings.

The question is not if AI will affect diagnosis, but how fast and under which constraints. As performance rises and commercial pressure grows, the path to superintelligence will depend on accountability, oversight and trust in healthcare systems.

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