Healthcare and pharmaceuticals are rapidly advancing with technological innovations, and the lack of understanding of AI algorithms poses a significant challenge in these fields. The need for more transparency in AI decision-making processes raises concerns about accountability, ethical implications, and regulatory compliance. As stakeholders in these critical sectors seek clarity and understanding, Analyzing Explainable AI in Healthcare and the Pharmaceutical Industry provides a reliable resource to discover new solutions.
This book serves as a comprehensive guide, unraveling the complexities of explainable artificial intelligence (XAI) and its pivotal role in transforming healthcare and pharmaceutical practices. Demystifying AI algorithms and revealing their decision-making mechanisms equips readers with the foundational knowledge needed to confidently navigate AI integration in these domains. From healthcare professionals to policymakers, its insights cater to a diverse audience, fostering cross-disciplinary collaboration and facilitating informed decision-making.
Through real-world case studies, ethical explorations, and regulatory insights, this book addresses the challenges posed by unclear AI processes and provides actionable solutions. Promoting transparency, fairness, and accountability empowers stakeholders to harness the full potential of XAI while mitigating risks and ensuring compliance. This book emerges as a vital resource, guiding the journey towards responsible and impactful AI adoption in healthcare and pharmaceutical settings.