DL in Healthcare: From Data to Diagnosis

Abstract

The management of modern healthcare systems presents a multifaceted challenge for governing bodies worldwide. The integration of Artificial Intelligence (AI), particularly Deep Learning (DL), into healthcare has sparked a transformative shift across clinical and biomedical domains. This chapter examines the crucial role of DL in enhancing healthcare delivery through improved data management, enhanced diagnostic precision, and refined therapeutic interventions. DL techniques have facilitated the rapid and accurate analysis of complex medical data, enabling earlier disease detection, tailored treatment plans, and optimized clinical workflows. By supporting clinicians in diagnostic decision-making and treatment selection, DL contributes to the advancement of personalized medicine, ultimately improving patient outcomes. This chapter delves into cutting-edge AI-based diagnostic systems, the application of DL in personalized therapeutic strategies, and its capacity to streamline disease management processes. Furthermore, it addresses the broader impact of AI in reducing disease morbidity and mortality, thereby reinforcing healthcare efficiency. However, despite these advancements, the chapter also critically examines the challenges impeding the widespread adoption of AI technologies. Ethical concerns, legal implications, algorithmic bias, data privacy issues, and limited awareness among stakeholders continue to be significant barriers. By providing a comprehensive overview of both the potential and limitations of DL in healthcare, this chapter aims to inform and guide future research, policy-making, and clinical applications in AI-driven healthcare systems.

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