significantly enhance clinical decision-making, leading to improved patient outcomes, reduced medical errors, and optimized healthcare delivery. This study explores the application of AI technologies—such as machine learning (ML), natural language processing (NLP), and deep learning—in supporting evidence-based clinical decisions across diverse medical domains. By processing vast amounts of structured and unstructured data from electronic health records (EHRs), diagnostic imaging, laboratory results, and clinical notes, AI systems can identify patterns, predict outcomes, and assist clinicians in making timely and accurate decisions. AI-driven clinical decision support systems (CDSS) provide real-time recommendations, alerts, and diagnostic assistance, particularly in high-stakes areas like oncology, cardiology, emergency medicine, and intensive care. These systems leverage predictive analytics to forecast disease progression, assess treatment efficacy, and personalize care pathways based on individual patient profiles. In addition, AI tools enable risk stratification, early warning systems, and triage support, improving operational efficiency and resource allocation. The study highlights key use cases where AI has successfully augmented human expertise, including early detection of sepsis, cancer diagnosis from medical imaging, medication error prevention, and management of chronic diseases. Furthermore, explainable AI (XAI) is emphasized as a critical component in building trust and transparency, ensuring that clinicians understand and validate AI-generated insights. Despite the potential, challenges remain in data interoperability, bias in training datasets, model interpretability, ethical concerns, and integration into clinical workflows. Addressing these barriers requires interdisciplinary collaboration, rigorous validation, and adherence to regulatory and ethical standards. In conclusion, leveraging AI in clinical decision-making holds transformative potential for modern healthcare systems. As technology matures, the synergistic collaboration between AI tools and healthcare professionals will be pivotal in delivering high-quality, efficient, and patient-centered care. Future efforts must focus on scalable implementation, continuous model improvement, and fostering clinician trust to fully realize AI’s impact in clinical settings.
Kuponiyi et al. (Sun,) studied this question.