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Artificial Intelligence (AI) is transforming healthcare management by improving decision-making, developing patient results, and maximizing productivity. Nevertheless, limitations in incorporating and analyzing AI-enabled Information Systems (ISs) impede consistent adoption, mainly because of the lack of established methodologies, diverse function evaluations, and the complication of healthcare data. To cope with these issues, this research proposes a novel taxonomy of AI-enabled IS for healthcare management. We analyzed 36 cutting-edge articles, dividing them into six main categories: Electronic Health Records (EHR), Clinical Decision Support Systems (CDSS), Medical Imaging Systems, Telemedicine Platforms, Drug Discovery, and Wearable Health Monitoring Systems. Our considered factors include datasets, simulation environments, and key function parameters such as accuracy, precision, specificity, sensitivity, and F1 score. The results show that EHR systems lead to accuracy (93.85%), precision (92.55%), specificity (92.73%), and F1 score (94.33%), while Wearable Health Monitoring Systems excel in sensitivity (94.58%) and Drug Discovery in dataset size (81,666).
Zahra Mohtasham‐Amiri (Tue,) studied this question.