Published Paper Details:

Artificial Intelligence (AI) and Machine Learning (ML) are driving transformational change across the life sciences, with major applications in drug discovery, precision medicine, diagnostics, and medical imaging. AI-powered algorithms accelerate drug discovery and development by rapidly analyzing complex datasets--reducing timelines from years to months--and supporting the identification of novel molecular compounds and drug repurposing opportunities. In diagnostics, AI systems can integrate genetic, clinical, and lifestyle data to predict disease progression, personalize treatment plans, and enable early interventions. ML facilitates biomarker identification for cancer and other diseases by leveraging large-scale genomic testing to tailor therapies for individual patients. Medical imaging has seen notable advancement, with AI detecting disease risks and abnormalities with improved accuracy, enhancing clinical outcomes. Furthermore, AI streamlines manufacturing, supply chain management, and clinical trial design through data automation and decision support. Despite these advances, challenges persist--such as data fragmentation, privacy issues, opacity in "black box" models, and limited interdisciplinary expertise. Future directions must emphasize explainable AI (XAI), secure and standardized data-sharing mechanisms, and integration with quantum computing, wearable sensing, and education that bridges technology and biology. In summary, AI and ML hold vast promise to reshape life sciences--accelerating innovation, improving patient care, and enabling personalized health solutions--while demanding security, transparency, and equitable access.