Embryonic Machine-Deep Learning, Smart Healthcare and Privacy Deliberations in Hospital Industry: Lensing Confidentiality of Patient’s Information and Personal Data in Legal-Ethical Landscapes Projecting Futuristic Dimensions

The incorporation of Machine and Deep Learning in Smart Healthcare technologies has revolutionized healthcare delivery from diagnostics to treatment planning. The vast amounts of data generated in this process which including patient health records, diagnostic imaging and treatment histories have led to an urgent need to reassess and fortify the legal and ethical frameworks surrounding patient data confidentiality. So, the strengthening encryption protocols adopting privacy-preserving algorithms and fostering a culture of data ethics are essential steps in maintaining patient privacy while leveraging the benefits of Machine and Deep Learning technologies. As hospitals increasingly rely on intelligent systems, ensuring the protection of sensitive information becomes paramount to maintaining public trust and upholding ethical standards. The increasing volume of patient data, coupled with the sophistication of cyber threats poses a significant challenge to maintaining data security. So, the proliferation of sensitive patient information and personal data in this digital era raises concerns about the confidentiality and privacy of such data. This chapter explores the various dimensions of the legal and ethical landscapes governing patient data privacy in the hospital industry, exploring the challenges and opportunities posed by Machine and Deep Learning in Smart Healthcare.