7 days ago
Mint Explainer: Can AI diagnose you better than a doctor?
Few sectors are witnessing AI's disruptive power as dramatically as healthcare. Trained on millions of historical patient records, advanced AI models are now matching—and in some cases surpassing—human expertise.
With real-time analysis across medical histories, imaging, and genetic profiles, AI transforms clinical complexity into diagnostic clarity. AI spots patterns even seasoned physicians might overlook.
Earlier this month, Microsoft unveiled a platform–Microsoft AI Diagnostic Orchestrator (MAI-DxO)—that can diagnose medical cases with an accuracy rate of up to 85.5%, far exceeding the 20% accuracy of experienced physicians under the same conditions. Mint explains the role and impact of AI in healthcare and diagnostics.
Does Microsoft's MAI-DxO improve diagnostic accuracy?
MAI-DxO leverages vast datasets, advanced probabilistic reasoning, and continuous learning from clinical cases to surpass human diagnostic accuracy. Unlike doctors who rely on experience and pattern recognition, MAI-DxO evaluates multiple variables simultaneously—lab results, symptoms, imaging—and predicts outcomes.
MAI‑DxO was tested on 304 real-world clinical scenarios sourced from the New England Journal of Medicine. In these cases, MAI‑DxO achieved an accuracy rate of 85.5%, surpassing the average 20% success rate of 21 experienced physicians from the UK and the US.
Instead of relying on a single model, MAI-DxO coordinates multiple large language models (LLMs) that interact like a team of doctors, reviewing, challenging, and refining each other's suggestions before settling on a final diagnosis.
Microsoft has emphasised that the tool is not designed to replace doctors, but to work alongside them.
What medical datasets does MAI-DxO use?
MAI-DxO is trained on anonymised patient records, peer-reviewed literature, medical guidelines and global clinical datasets from hospitals and research institutions.
Its database spans symptoms, biomarkers, comorbidities, and disease trajectories across demographics, allowing it to contextualize findings in real-world scenarios.
So if a patient suddenly feels numbness on her left side, it could indicate stroke, nerve damage, heart problem or some other issue. Doctors will wait for test results (blood and scans) to diagnose the problem, while the AI platform will interpret it faster, helping the doctor make quicker decisions.
MAI-DXO's strength lies in its ability to absorb medical nuance across specialties—mirroring the cumulative expertise of several doctors and researchers.
Will AI replace doctors?
AI will not replace but complement doctors and other health professionals. They need to navigate ambiguity and build trust with patients. Clinical roles will evolve with AI, giving medical staff the ability to automate routine tasks, identify diseases earlier, personalize treatment plans, and potentially prevent some diseases altogether.
For consumers, they will provide better tools for self-management and shared decision-making. These will be particularly helpful in remote areas where there's a shortage of doctors.
MAI-DxO operates on statistical inference rather than intuition. It learns to recognise patterns and contextual subtleties from data, simulating judgment. While human intuition draws on tacit knowledge—emotion, empathy, gut feel—AI taps structured data and outcome probabilities.
Platforms like MAI-DxO can augment decision-making. These are especially useful when doctors second-guess themselves—making MAI-DxO an ally, not a rival.
What risks do AI-driven diagnostics pose for patient care?
Key risks include overreliance, bias from skewed training data, and lack of transparency in AI reasoning. If doctors defer judgment too easily, subtle clinical clues could be overlooked. Additionally, AI platforms recommendations may vary based on demographics or comorbidities not well represented in its training data, potentially exacerbating health disparities.
For instance, if they are trained only on American or European patient data, they might overlook or misinterpret common conditions among patients in South Asia, say tuberculosis (TB) or type 2 diabetes or complications caused by chewing tobacco.
There's also the risk of false positives and patient anxiety. Most critically, AI tools must be accountable — patients need clarity on how diagnoses are made. The challenge lies in integrating AI without ignoring the human touch in care.
What are the regulatory frameworks for AI deployment in hospitals?
In India, every AI tool must be approved by the Delhi-based Central Drugs Standard Control Organisation (CDSCO)— equivalent to the US Food and Drug Administration (FDA). Globally, regulatory bodies like the FDA (US), European Medicines Agency (EMA-Europe) are developing guidelines for AI in medicine.
Back home, AI platforms need clearance as a software as a medical device (SaMD), requiring evidence of clinical safety, efficacy, and data privacy compliance.
What is the role of AI in the future of medicine?
Current developments position them as early warning systems, helping doctors rather than making final decisions. According to the World Economic Forum 4.5 billion people are currently without access to essential healthcare services and a health worker shortage of 11 million is expected by 2030. AI has the potential to help bridge that gap.
AI can also assist paramedics in situations where a patient is being transferred via an ambulance. AI models trained on factors such as a patient's mobility, pulse and blood oxygen levels, chest pain, etc., can relay information to doctors, helping them make decisions faster.
AI can detect early signs in an individual that are likely to result in diseases like Alzheimer's, heart disease, kidney disease, and so on. This could help doctors suggest preventive action.
Can MAI-DxO evolve into a real-time medical assistant across specialties?
At present, MAI-DxO is a demonstration of AI capability and research. With real-time access to patient records, labs, and imaging, MAI-DxO could become an assistant, offering diagnostic suggestions, tracking progress, and alerting doctors to anomalies as they emerge.
Integrated into electronic health systems, it could support rounds, triage, and even remote consultations. Much like AI embedded medical devices (ultrasound devices, bedside X-ray machines) are now being used in some hospitals, including Max healthcare, to interpret scans and assist doctors. The key lies in continual updates, feedback loops and building trust.
