Quantum Computing for Healthcare AI: Adaptive Diagnosis Models that Evolve with Patient Data and Medical Knowledge
Abstract
The integration of quantum computing and artificial intelligence (AI) holds transformative potential for healthcare by enabling adaptive diagnostic models that evolve with dynamic patient data and expanding medical knowledge. Leveraging quantum principles such as superposition and entanglement allows these models to efficiently process complex, high-dimensional healthcare datasets, improving precision in disease diagnosis and personalized treatment strategies. The proposed hybrid architecture combines quantum machine learning algorithms with classical AI components for interpretability and continuous refinement through real-time data ingestion, mitigating model drift and maintaining clinical relevance. This approach demonstrates significant promise in enhancing diagnostic accuracy and fostering intelligent healthcare solutions responsive to individual patient needs and medical advances [1]– [3].
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