How Reliable Is Medical AI Diagnostic Accuracy in Real Clinical Settings?
I recently read a paper on AI for early lung cancer diagnosis, which reported 98% accuracy on a specific dataset. However, I've also heard that when used in actual hospitals, performance can drop due to data bias or environmental differences.
For example, some studies show that AI for skin cancer diagnosis in dermatology is optimized for white skin, leading to higher misdiagnosis rates for Asian patients.
I'm curious about how trustworthy medical AI is in real clinical settings, and how regulatory and ethical issues will be addressed going forward. I'd love to hear opinions from people working in this field.
5 answers
The accuracy of medical AI heavily depends on the dataset. A figure like 98% is often achieved in controlled environments, but in real clinical settings, it can drop to 70-80% due to factors such as patient diversity, image quality, and equipment differences. Models lacking sufficient Asian data, in particular, require caution.
I work in radiology and use AI as a supportive tool. When diagnosing lung cancer, AI identifies abnormal findings, and I double-check them. While the sensitivity is high, the specificity is low, resulting in quite a few false positives. Ultimately, human judgment remains essential.
The issue of racial bias in skin cancer AI is indeed serious. I've heard that AI models developed in Korea are optimized for domestic data, so retraining is necessary when exporting them overseas. In terms of regulation, the verification of racial diversity is being strengthened during the approval process by the FDA or the Ministry of Food and Drug Safety.
I am a guardian of a patient who has used an AI diagnostic tool. At the hospital, I heard the results of an AI lung nodule analysis, but the doctor said to 'use it only as a reference.' It seems the reality is that it's still considered more of an auxiliary tool rather than something to fully trust.
Ethical issues revolve around data privacy and accountability. It's still not clearly established who will be responsible when AI misdiagnoses, or how patient consent should be obtained. In Europe, guidelines are being developed by combining GDPR with medical device regulations, and South Korea is seeing similar movements.