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How does AI differ from traditional healthcare technologies?

AI fundamentally differs from conventional healthcare IT through learning, adaptation, and intelligence. Traditional systems follow fixed rules: if patient has symptom X and lab value Y, then suggest diagnosis Z. These rule-based systems become unwieldy with complexity and cannot handle nuanced cases or learn from new data. AI systems learn from data patterns, identifying relationships too complex for manual rule creation. They improve over time: as they process more cases, accuracy increases without human reprogramming. Traditional systems remain static unless developers manually update code. Predictive capabilities distinguish AI: forecasting patient deterioration, readmission risk, disease progression, and treatment responses rather than just reporting current state. Traditional systems are descriptive (showing what happened) while AI is predictive (forecasting what will happen) and prescriptive (recommending what to do). Adaptability means AI handles ambiguity, incomplete data, and novel situations more gracefully than rigid traditional systems. AI processes unstructured data (clinical notes, images, pathology slides) that traditional systems cannot effectively utilize. However, AI requires more data for training, faces 'black box' interpretability challenges, and needs continuous monitoring for drift (performance degradation over time). Traditional systems are more transparent and predictable but less powerful. The future involves hybrid approaches: rule-based systems for well-understood workflows augmented by AI for complex pattern recognition and prediction.