Information Window as a Methodology for Assessing the Safety and Effectiveness Balance of Artificial Intelligence Medical Systems

Abstract

The article examines methodological approaches to assessing the safety and clinical efficacy of artificial intelligence (AI) systems in healthcare. A comparative analysis of the “benefit–risk” concept in pharmacology and medical AI systems is conducted to adapt the concept of the therapeutic window to information technologies. A model of the information window is proposed as a formalized tool for evaluating the balance between expected benefits and potential risks; this concept refers to the range of decision-making parameters within which an AI system maintains an optimal balance between safety and efficacy. Current regulatory requirements, technical performance indicators, ethical aspects, and social implications are taken into account. The necessity of phased testing of AI systems, analogous to clinical trials of pharmaceuticals, is substantiated. The prospects for developing international standards to define acceptable benefit–risk indices are outlined. The importance of continuous auditing, model updating, and establishing mechanisms of accountability is emphasized. The proposed approach contributes to the development of unified standards for the safe and effective implementation of AI in medical practice

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