The availability of data is a condition for the development of AI. This is no different in the context of healthcare-related AI applications. Healthcare data are required in the research, development, and follow-up phases of AI. In fact, data collection is also necessary to establish evidence of compliance with legislation. Several legislative instruments, such as the Medical Devices Regulation and the AI Act, enacted data collection obligations to establish (evidence of) the safety of medical therapies, devices, and procedures. Increasingly, such health-related data are collected in the real world from individual data subjects. The relevant legal instruments therefore explicitly mention they shall be without prejudice to other legal acts, including the GDPR. Following an introduction to real-world data, evidence, and electronic health records, this chapter considers the use of AI for healthcare from the perspective of healthcare data. It discusses the role of data custodians, especially when confronted with a request to share healthcare data, as well as the impact of concepts such as data ownership, patient autonomy, informed consent, and privacy and data protection-enhancing techniques.

Chapter 8 concludes the book by proposing ways to improve decision-making in relation to sharing linked data for research. It considers improvements in a number of areas: the decision-making framework of interests, values, and rights; the decision-making criteria and conditions; the decision makers who are best placed to make each decision; and the decision-making process. The chapter sets out the interests, values and rights that should frame decisions in this sphere, not all of which are currently represented in decision-making frameworks. It provides a list of decision-making criteria and considerations that should be taken into consideration by the relevant decision makers. The chapter distinguishes between ethical decisions, which should be made by ethics committees and governance decisions, which should be made data custodians. Finally, the chapter makes recommendations for a decision-making process that will be efficient, transparent, accountable and collaborative. This process is designed to lead to better decisions and to ensure that both the decision-making process and the decisions themselves develop and sustain the social licence needed to support the important enterprise of research using linked data.

Chapter 8 concludes the book by proposing ways to improve decision-making in relation to sharing linked data for research. It considers improvements in a number of areas: the decision-making framework of interests, values, and rights; the decision-making criteria and conditions; the decision makers who are best placed to make each decision; and the decision-making process. The chapter sets out the interests, values and rights that should frame decisions in this sphere, not all of which are currently represented in decision-making frameworks. It provides a list of decision-making criteria and considerations that should be taken into consideration by the relevant decision makers. The chapter distinguishes between ethical decisions, which should be made by ethics committees and governance decisions, which should be made data custodians. Finally, the chapter makes recommendations for a decision-making process that will be efficient, transparent, accountable and collaborative. This process is designed to lead to better decisions and to ensure that both the decision-making process and the decisions themselves develop and sustain the social licence needed to support the important enterprise of research using linked data.