The Role of Machine Learning in Predictive Medicine: A Paradigm Shift in Clinical Decision-Making
Mots-clés :
machine learning, predictive medicine, clinical decision-making, personalized healthcare, electronic health records, precision medicine, healthcare data analytics, algorithmic bias, medical AI, real-time diagnosisRésumé
Machine learning (ML) has emerged as a transformative force in predictive medicine, reshaping the landscape of clinical decision-making by enabling the early detection, diagnosis, and treatment of diseases. Through the analysis of vast and complex datasets, ML algorithms can identify patterns and correlations that elude traditional statistical methods, thereby facilitating more accurate and individualized healthcare solutions. This paradigm shift is particularly evident in areas such as oncology, cardiology, and neurology, where predictive models are being employed to assess disease risk, forecast progression, and recommend personalized treatment plans. The integration of electronic health records (EHRs), genomic data, imaging studies, and wearable sensor outputs allows ML systems to provide real-time insights that assist clinicians in making data-driven decisions.
