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Dr. R. Shanthi is an Associate Professor and Head of the Department of Mathematics at Meenakshi College of Arts and Science, affiliated with Meenakshi Academy of Higher Education and Research, Chennai, India. Her responsibilities include mathematics education and departmental leadership. Her academic role contributes to teaching and institutional development. Her contribution to this chapter concerns computational approaches to personalized therapeutic recommendation and optimization.
Artificial Intelligence (AI) is progressively being investigated to enhance individualised therapeutic suggestions and treatment refinement by amalgamating patient-specific details, medical history, diagnostic results, treatment outcomes, and pertinent biomedical facts. Traditional therapy methodologies frequently depend on established clinical protocols and aggregate data, which may inadequately consider personal variations in disease traits, coexisting conditions, medication sensitivity, and treatment efficacy. This chapter delineates a conceptual framework for individualised therapeutic suggestions and treatment enhancement, encompassing data amalgamation, patient characterisation, treatment-response forecasting, recommendation formulation, optimisation methodologies, and clinical assessment. It further addresses issues related to data integrity, algorithmic prejudice, interpretability, confidentiality, security, and clinical validation. Its therapeutic efficacy must be confirmed via stringent validation, forward-looking assessment, and suitable regulatory supervision prior its implementation in medical environments.