A doctor reads a patient's report while a computer analyses an X-ray and highlights the part that needs a closer look. Artificial intelligence is already used this way in medicine, helping doctors analyse X-rays, CT scans, MRIs, eye images, skin lesions and other medical data. So will AI one day take the doctor's place? A report in Amar Desh looks for the answer.
AI diagnosis, the report explains, is essentially a technology for finding abnormalities in data. A model is trained on thousands of medical images labelled with and without signs of disease, and learns to recognise the patterns. Given a new chest X-ray, it can point to unusual shadows; given a retinal image, it can flag signs of diabetes-related eye damage.
Its great advantage is speed. In busy hospitals, AI can sort out suspicious reports and bring them to a doctor's attention first, and it can sometimes catch small changes the human eye may miss. Beyond imaging, machine learning and deep learning have been widely studied for assessing heart risk, detecting eye disease and analysing some features of cancer.
But AI can be wrong. If real-world patients, machines or data differ from those a model was trained on, its accuracy drops, and a model built on one country's hospital data may not work the same way in another. And a flagged abnormality does not confirm disease; it is only a signal for further tests.
The report's point is that AI is not a machine that names the disease but an aid that helps doctors spot possible problems faster; the final diagnosis and the decision on treatment remain the doctor's.
Photo: Stillwaterising / Wikimedia Commons (CC0)
Source: Amar Desh
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