Bridging the gap in AI integration: enhancing clinician education and establishing pharmaceutical-level regulation for ethical healthcare

Recently the role of AI in healthcare has been deeply studied and discussed in scientific literature. Promising applications of artificial intelligence and machine learning (AI/ML) are revolutionizing both clinical and administrative domains, with significant advancements demonstrated in drug discovery, precise analysis and interpretation of radiological images, early and accurate sepsis detection, efficient hospital resource management, automated documentation of clinical encounters and decision support system (DSS). These use cases underscore the immense potential of AI/ML to enhance efficiency, accuracy, and outcomes across the healthcare spectrum (1). However, a very recent article raises essential considerations about the adoption and regulation of AI in clinical settings (2). Therefore, the integration of Artificial Intelligence (AI) in healthcare presents numerous opportunities and challenges. Conversely, there is a significant gap between clinician education regarding AI and the regulatory measures necessary for ethical deployment. To address this gap effectively, a structured, organized approach must be followed, encompassing clearly defined steps for both the education of clinicians and the establishment of rigorous regulatory frameworks. This paper argues that bridging this gap requires a dual approach: enhancing clinicians’ understanding of AI technologies and treating AI systems as rigorously as pharmaceuticals through strict regulatory processes. By doing so, we can foster ethical and effective AI integration into clinical practice, ensuring patient safety and better healthcare outcomes. Here, we outline four key considerations that should guide the planning and regulation of AI integration in healthcare.