AI Predicted to Boost Doctor Productivity Six-Fold
Dr. Devi Prasad Shetty of Narayana Health predicted that AI will enable doctors to manage six times more patients without being replaced. As an example, he suggested a dermatologist's patient capacity could increase from 3,000-5,000 to 30,000, making healthcare safer and more accessible.
- Dr. Shetty's Narayana Health has a history of adopting technology to reduce costs, moving its operations to Microsoft Azure in 2015 and later implementing real-time data analytics to reduce reporting man-hours by 70%. The hospital group has also deployed AI tools for tasks like interpreting X-rays and, more recently, launched "Aira," an AI-powered tool to reduce doctor paperwork. - In August 2025, Narayana Health developed an AI model with its Medha AI division that can predict heart function from an ECG in 10 seconds, a significant step for a country where 10 million people suffer from heart failure. In external validation across over 57,000 patients, the system identified 97% of individuals with severely reduced heart function an average of 58 days earlier than traditional methods. - The technology behind AI dermatology often uses deep learning systems trained on large image datasets. For instance, Google's model was trained on about 65,000 images and can identify 288 conditions, achieving accuracy on par with U.S. board-certified dermatologists. However, researchers have raised concerns about the lack of darker skin tones in the training data, which could affect diagnostic reliability for diverse populations. - VC investment in healthcare AI has shown strong momentum, with AI-enabled startups capturing 46% of all healthcare venture investment in 2025. Mega-deals of over $300 million accounted for 40% of total healthcare AI spending that year, reflecting significant capital requirements for generative AI solutions. - In the Turkish startup ecosystem, several early-stage health-tech companies are emerging, such as Mamosis, an AI-powered cancer diagnosis software, and Albert, a voice-based health assistant for medication management. In September 2025, Turkish founder Taylan Ozdemir Aydin's AI-driven pharmaceutical data analysis startup, Flyway Health, secured $1 million in pre-graduation funding. - A key technical challenge for diagnostic AI is maintaining accuracy when analyzing compressed images, such as photos of X-rays sent via mobile apps, which can lose up to 95% of their quality. This data loss can lead to prediction instability and misinterpretation, making the model's "explainability"—its ability to show how it reached a conclusion—a critical area of research. - The adoption of AI in healthcare faces significant hurdles beyond technology, including the integration of tools into clinical workflows, patient data privacy regulations, and the potential for algorithms to perpetuate biases present in historical data.
