Hello, I'm curious about the fundamental logic behind AI-assisted medical diagnosis systems. Specifically, how do machine learning models identify diseases? How are datasets prepared, and which algorithms are commonly used in these systems? Additionally, what methods are employed to improve the accuracy rates of these systems? Could you also share your experiences on the topic?
How do AI-based diagnostic systems work?
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AI-based diagnostic systems can also be thought of like radar. Just as radar picks up signals from the environment to identify objects, AI models "diagnose" diseases by analyzing medical data (such as X-rays, MRI results, and lab tests). At their core, machine learning models (like CNNs that use deep learning) rely on patterns learned from large datasets. For example, a lung cancer diagnosis system examines thousands of normal and cancerous lung X-rays, statistically identifies the visual features of cancer (such as the shape and density of nodules), and compares new X-rays to these patterns.
Datasets can be thought of like the question-and-answer examples in a textbook. Anonymous patient data (X-rays, CT scans, blood tests) and their correct diagnoses from hospitals are used. The key here is diversity in the data—if different races, genders, age groups, and even device brands aren’t included, the system can’t generalize well. The most commonly used algorithms include CNNs (for image analysis), Random Forest or XGBoost (for classifying complex datasets), and Transformer-based models (for analyzing documentation). To improve accuracy, three things are typically done: collecting more and diverse data (such as through international hospital data pools), continuously updating the model using "active learning," and integrating human expertise—especially in areas where AI falls short. Similarly, in automatic parking systems, vehicles learn from camera and sensor data to park, operating on a similar principle.