AI's impact on the medical field is a double-edged sword, offering potential relief to some while posing risks of misdiagnosis to others. Recent reports highlight both the successes and shortcomings of using AI in healthcare, as individuals experiment with AI tools like ChatGPT and Grok for medical insights.

OpenAI president Greg Brockman has noted the positive impact of AI, citing examples of ChatGPT helping individuals resolve long-standing health issues. One user reported a significant reduction in chronic back pain after inputting detailed information into ChatGPT, leading to a breakthrough that traditional treatments had failed to achieve.

However, the use of AI in medical diagnosis is not without its pitfalls. Elon Musk's AI chatbot Grok, while praised by some users for providing helpful feedback on medical scans, has also been responsible for misdiagnoses, raising concerns about over-reliance on AI for medical interpretation. Musk had encouraged users to submit medical images to Grok for analysis, touting its accuracy.

A study conducted by researchers at Osaka Metropolitan University's Graduate School of Medicine assessed the diagnostic performance of generative AI, revealing an average diagnostic accuracy of 52.1%. While newer AI models performed comparably to non-specialist doctors, specialists demonstrated significantly higher accuracy. Further research has also exposed disparities in AI systems, with treatment recommendations varying based on a patient's socioeconomic and demographic profile. High-income patients were more likely to be recommended advanced tests, while low-income patients were often advised against further testing.

Despite these challenges, AI has demonstrated its potential in assisting medical professionals. Mumbai-based AI startup Qure.ai successfully diagnosed tuberculosis in a patient whose symptoms had perplexed multiple doctors. Experts agree that AI can play a valuable role in healthcare, but its effectiveness depends on the quality and diversity of the data it is trained on. Caution is advised when using AI for self-diagnosis, as faulty health information could lead to unnecessary tests and costly care.

Suchi Saria, director of the machine learning and healthcare lab at Johns Hopkins University, emphasizes the importance of caution when using AI for self-diagnosis. The consensus is that while AI holds promise, it is essential to approach its use in healthcare with careful consideration and a reliance on expert medical opinion.