AI's medical hits and misses: Some patients get relief from years of suffering; others are misdiagnosed
OpenAI
president and cofounder Greg Brockman has claimed that artificial intelligence is beginning to make a meaningful difference in people's lives, particularly in areas such as medical diagnoses.
In a recent post on X, Brockman said, 'I'm hearing more and more stories of ChatGPT helping people fix longstanding health issues.'
He followed up with an anecdote involving a ChatGPT user, who had suffered from chronic back pain for over a decade. Despite trying physiotherapy and other treatments, relief had remained elusive.
— gdb (@gdb)
With all else failing, the individual fed detailed information into ChatGPT — including history, pain triggers, and exercises tried. The user claimed this led to a breakthrough, with pain levels decreasing by 60–70%.
Not just ChatGPT
Back in November,
Elon Musk
's AI chatbot Grok made headlines for similar reasons. Users had begun uploading medical scans, including MRIs and X-rays, seeking diagnostic insights.
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Musk had encouraged this and urged users to 'try submitting x-ray, PET, MRI, or other medical images to Grok for analysis', adding that the tool 'is already quite accurate and will become extremely good'.
Some reported helpful feedback. However, others were misdiagnosed, highlighting the risks of relying solely on AI for medical interpretation.
Promise vs precision
The role of
AI in healthcare
remains a widely debated topic, raising questions about its potential and its pitfalls.
A study led by Dr Hirotaka Takita and Associate Professor Daiju Ueda at Osaka Metropolitan University's Graduate School of Medicine explored the diagnostic performance of generative AI. Their research, reported by IANS, found that the average diagnostic accuracy was 52.1%, with newer models performing on par with non-specialist doctors.
However, specialists still outperformed AI significantly, maintaining a 15.8% higher diagnostic accuracy.
Meanwhile, a Reuters-reported study revealed troubling disparities. AI systems were shown to recommend different treatment paths for the same condition based purely on a patient's socioeconomic and demographic profile.
For example, advanced tests such as CT scans or MRIs were more often suggested for high-income patients, while low-income patients were more frequently advised against further testing — a mirror to the current inequalities in healthcare.
On the other hand, in October last year,
ET reported
that Mumbai-based AI startup Qure.ai successfully assisted in diagnosing tuberculosis in a patient, whose symptoms had confused several doctors.
Experts agree that AI has a role to play in assisting medical professionals, but its impact hinges on the quality and diversity of the data it is trained on. Caution is advised when using AI for self-diagnosis.
'Imperfect answers might be okay for people purely experimenting with the tool, but getting faulty health information could lead to tests or other costly care you don't actually need,' said Suchi Saria, director of the machine learning and healthcare lab at Johns Hopkins University.
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