Artificial intelligence (AI) is a powerful tool for several healthcare tasks. AI tools are suited to optimize predictive models in medicine. Ethical debates about AI’s extension of the predictive power of medical models suggest a need to adapt core principles of medical ethics. This article demonstrates that a popular interpretation of the principle of justice in healthcare needs amendment given the effect of AI on decision-making. The procedural approach to justice, exemplified with Norman Daniels and James Sabin’s accountability for reasonableness conception, needs amendment because, as research into algorithmic fairness shows, it is insufficiently sensitive to differential effects of seemingly just principles on different groups of people. The same line of research generates methods to quantify differential effects and make them amenable for correction. Thus, what is needed to improve the principle of justice is a combination of procedures for selecting just criteria and principles and the use of algorithmic tools to measure the real impact these criteria and principles have. In this article, the author shows that algorithmic tools do not merely raise issues of justice but can also be used in their mitigation by informing us about the real effects certain distributional principles and criteria would create.

Objective: The Israeli National Health Insurance Law stipulates a National List of Health Services (NLHS) to which all residents are entitled from their HMOs. This list has been updated annually for almost a decade using a structured review and decision-making process. Although this process has been described in detail in previous papers, none of these have fully addressed legitimacy and fairness. We examine the legitimacy and fairness of the process of updating the NLHS in Israel.

Methods: We assessed the priority-setting process for compliance with the four conditions of accountability for reasonableness outlined by Daniels and Sabin (relevance, publicity, appeals, and enforcement). These conditions emphasize transparency and stakeholder engagement in democratic deliberation.

Results: Our analysis suggests that the Israeli process for updating the NLHS does not fulfill the appeals and enforcement conditions, and only partially follows the publicity and relevance conditions, outlined in the accountability for reasonableness framework. The main obstacles for achieving these goals may relate to the large number of technologies assessed each year within a short time frame, the lack of personnel engaged in health technology assessment, and the desire for early adoption of new technologies.

Conclusions: The process of updating the NLHS in Israel is unique and not without merit. Changes in the priority-setting process should be made to increase its acceptability among the different stakeholders.