Latest news with #MAIDiagnosticOrchestrator
India Today
5 days ago
- Health
- India Today
Microsoft claims its AI tool can diagnose complex cases better than doctors
Microsoft claims that it has developed an AI tool that, in a recent experiment, diagnosed patients with four times more accuracy than human technology, called MAI Diagnostic Orchestrator (MAI-DxO), works by combining multiple advanced AI models, including ChatGPT, Google's Gemini, Anthropic's Claude, Meta's Llama, and xAI's Grok. The system mimics a team of doctors working together, sharing opinions and debating symptoms before reaching a test the system, researchers used 304 real-life case studies published in the New England Journal of Medicine. These were turned into a series of patient scenarios, where the AI had to figure out the illness just as a doctor would by analysing symptoms, ordering tests, and narrowing down possibilities step-by-step. The results were surprising: the AI system correctly diagnosed 80% of the cases, compared to just 20% by a group of human doctors. And it wasn't just about accuracy. The AI also managed to lower the cost of diagnosis by about 20%, by choosing more affordable tests and avoiding unnecessary procedures.'This is a genuine step toward medical superintelligence,' said Mustafa Suleyman, CEO of Microsoft AI, highlighting the tool's potential to transform healthcare taking a big step towards medical superintelligence. AI models have aced multiple choice medical exams – but real patients don't come with ABC answer options. Now MAI-DxO can solve some of the world's toughest open-ended cases with higher accuracy and lower costs. Mustafa Suleyman (@mustafasuleyman) June 30, 2025advertisementAccording to the company, as demand for healthcare continues to grow, costs are rising at an unsustainable pace, and billions of people face multiple barriers to better health, including inaccurate and delayed AI has already been used to help doctors interpret medical scans, this latest development suggests it could take on broader diagnostic roles, possibly becoming a first point of contact for patients in the involved in the project say this could help reduce healthcare costs and speed up access to care. 'Our model performs incredibly well—both getting to the diagnosis and doing so cost-effectively,' said Dominic King, a Microsoft vice like all AI systems, these tools must be carefully monitored. There are concerns about whether they work equally well across different populations, since much of the training data may be skewed toward certain groups."This research is just the first step on a long, exciting journey. We're excited to keep testing and learning with our healthcare partners in pursuit of better, more accessible care for people everywhere," Suleyman said.- EndsMust Watch
Arabian Post
6 days ago
- Health
- Arabian Post
Microsoft Unveils AI Diagnostician Surpassing Human Clinicians
Microsoft has introduced the MAI Diagnostic Orchestrator, an advanced AI system that diagnoses complex medical conditions with four times the accuracy of unaided doctors. In a trial using 304 challenging case studies from the New England Journal of Medicine, the tool achieved an 85.5% success rate, compared with around 20% for physicians barred from referencing external resources. The innovation rests on a multi-agent 'orchestrator' framework that mimics a panel of five specialists, each performing distinct functions—formulating hypotheses, recommending tests, and collating evidence. The system operates using a 'chain‑of‑debate' methodology, requiring its AI components to methodically justify each diagnostic step. When integrated with OpenAI's o3 model, performance peaked; other large language models—Meta, Anthropic, Google and xAI—also saw significant improvements under this architecture. Microsoft's AI health arm, led by Mustafa Suleyman and including deep learning figures formerly from DeepMind, emphasises that the platform is model‑agnostic. Nevertheless, Suleyman described outcomes as 'dramatically better than human performance: faster, cheaper and four times more accurate'. Dominic King, another former DeepMind health researcher now at Microsoft, praised the 'landmark' nature of the work, while cautioning it remains in pre‑clinical phases and has not yet undergone peer review. ADVERTISEMENT The technology not only enhances diagnostic precision but optimises test utilisation, reportedly cutting testing costs by up to 20%. In one illustrative example, MAI‑DxO attained accurate diagnoses with fewer and less expensive investigations. It is anticipated that the system will be rolled into Microsoft's consumer‑facing platforms such as Copilot and Bing, which manage tens of millions of health‑related inquiries daily. Yet the path to clinical integration faces significant hurdles. Experts highlight that the trialistic setting—artificial and devoid of real‑world complexity—differs vastly from live medical environments. Cardiologist Eric Topol of the Scripps Research Translational Institute noted that, while indicative of generative AI's potential, the work 'was not done in the setting of real world medical practice'. Microsoft itself stresses the need for extensive validation before deployment in clinical care. The development also intersects with broader dynamics in AI. As Microsoft seeks to extend its exclusive partnership with OpenAI—investing nearly $14 bn—the tension over platform control is evident. Despite being model‑agnostic, Microsoft's reliance on OpenAI's o3 model for peak performance draws renewed attention to the strategic leverage being negotiated. Alongside the Microsoft leap, other healthcare AI initiatives continue to gain momentum. Google's DeepMind recently launched AlphaGenome, a model targeting non‑coding DNA regions with implications for genetic illness; separately, UK trials report AI detection of epilepsy and other conditions via imaging and health record analysis. These developments underscore a growing shift from theoretical promise to practical application. Microsoft's MAI‑DxO represents a pivotal juncture—not as a medical panacea but as a catalyst in the transformation of diagnostic medicine. Its broader significance lies not only in outperforming unaided clinicians in controlled trials, but in pointing towards a future where AI supports medical professionals in managing complexity and resource constraints.
