@nydia

AI-assisted diagnosis reduces misdiagnosis by leveraging machine learning models trained on vast datasets of medical images, lab results, and patient histories. These systems support clinicians by providing second opinions, highlighting subtle abnormalities, and predicting disease risk with high accuracy. By standardizing interpretation and minimizing human error, AI tools help identify conditions earlier and more reliably. Continuous learning and integration with electronic health records enhance diagnostic precision. However, they complement rather than replace clinicians, as human judgment remains critical for holistic patient evaluation.

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