AI and Machine Learning for Predictive Healthcare and Disease Management
DOI:
https://doi.org/10.63282/3050-922X.ICRCEDA25-121Keywords:
Artificial Intelligence, Machine Learning, Predictive Healthcare, Disease Management, Healthcare Analytics, Early Disease Detection, Predictive Models, Personalized Medicine, Healthcare Data, AI in HealthcareAbstract
Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing the landscape of predictive healthcare and disease management by enabling data-driven insights, early diagnosis, and personalized treatment strategies. These technologies process vast volumes of healthcare data ranging from electronic health records (EHRs) and medical imaging to genomics and wearable sensor data to identify patterns and trends that may not be evident through traditional clinical analysis. By training algorithms on historical patient data, AI and ML models can accurately forecast the onset or progression of diseases such as diabetes, cardiovascular conditions, and cancer, allowing for timely and targeted interventions. In chronic disease management, AI helps monitor patients in real-time, providing alerts when health parameters deviate from the norm. ML algorithms can also stratify patients based on risk levels, enabling personalized treatment plans and resource optimization. For instance, predictive models in oncology can recommend treatment regimens based on tumor genomics, while in cardiology, ML can detect arrhythmias from ECG data with high accuracy. Despite their immense potential, the integration of AI and ML into clinical practice is not without challenges. Data privacy concerns arise due to the sensitive nature of health information, and there is a need for robust data governance and anonymization protocols. Additionally, many AI models function as “black boxes,” offering limited interpretability raising concerns about trust and accountability in clinical decisions. Regulatory bodies are still evolving frameworks to evaluate the safety, efficacy, and ethical use of these technologies. Looking ahead, the future of AI and ML in healthcare lies in developing explainable, ethically aligned models that integrate seamlessly with clinical workflows. By combining clinical expertise with intelligent systems, healthcare delivery can become more proactive, precise, and patient-centered ultimately leading to improved outcomes, reduced costs, and more equitable access to care
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