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This paper reviews the transformative impact of AI across the pharmaceutical industry, from drug discovery using tools like AlphaFold to optimizing clinical trials and enhancing manufacturing processes. AI-driven predictive modeling improves patient recruitment and trial design, while also enabling personalized medicine through analysis of genomic and clinical data. However, the paper emphasizes the critical need for addressing challenges related to data quality, algorithmic transparency, regulatory compliance, and ethical governance to ensure safe and effective AI deployment.
AI's promise to revolutionize pharma is tempered by the urgent need for explainable, validated models that can navigate regulatory hurdles and ethical concerns.
Artificial Intelligence (AI) is increasingly transforming the pharmaceutical industry by streamlining processes across drug discovery, clinical development, manufacturing, and post-marketing surveillance. In drug discovery, platforms such as DeepMind鈥檚 AlphaFold enable accurate protein structure prediction and identification of promising drug candidates, thereby reducing timelines and costs traditionally associated with early-stage research. Clinical trials benefit from AI-driven predictive modeling, which improves patient recruitment, optimizes trial design, and enhances real-time monitoring through wearable devices and IoT sensors, ultimately increasing efficiency and success rates. In manufacturing and supply chain management, AI supports advanced forecasting, automation, and blockchain-based traceability, reducing waste and mitigating risks of counterfeit drugs. Personalized medicine is strengthened by AI tools that analyze genomic and clinical data to generate individualized treatment plans, improving therapeutic outcomes while minimizing adverse effects. Despite these advances, challenges remain in ensuring data quality, algorithmic transparency, regulatory compliance, and ethical governance. Regulatory agencies such as the FDA and EMA emphasize the need for explainable and validated AI models before widespread adoption. This review critically evaluates the innovations and limitations of AI in the pharmaceutical sector, highlighting its potential to improve healthcare delivery while underscoring the importance of rigorous validation and regulatory oversight.
