[AI-enabled clinical decision support systems: challenges and opportunities].
Authors
Affiliations (6)
Affiliations (6)
- Institut für Künstliche Intelligenz und Informatik in der Medizin, Technische Universität München, School of Medicine and Health, TUM Klinikum, München, Deutschland.
- Google Deutschland GmbH, München, Deutschland.
- Institut für Diagnostische und Interventionelle Radiologie, Technische Universität München, School of Medicine and Health, Klinikum rechts der Isar, TUM Klinikum, München, Deutschland.
- Institut für Kardiovaskuläre Radiologie und Nuklearmedizin, Technische Universität München, School of Medicine and Health, Deutsches Herzzentrum München, TUM Klinikum, München, Deutschland.
- Institut für Künstliche Intelligenz und Informatik in der Medizin, Technische Universität München, School of Medicine and Health, TUM Klinikum, München, Deutschland. [email protected].
- Frauenklinik und Poliklinik, Technische Universität München, School of Medicine and Health, Klinikum rechts der Isar, TUM Klinikum, TUM Klinikum, Ismaninger Str. 22, 81675, München, Deutschland. [email protected].
Abstract
Clinical decision-making is inherently complex, time-sensitive, and prone to error. AI-enabled clinical decision support systems (CDSS) offer promising solutions by leveraging large datasets to provide evidence-based recommendations. These systems range from rule-based and knowledge-based to increasingly AI-driven approaches. However, key challenges persist, particularly concerning data quality, seamless integration into clinical workflows, and clinician trust and acceptance. Ethical and legal considerations, especially data privacy, are also paramount.AI-CDSS have demonstrated success in fields like radiology (e.g., pulmonary nodule detection, mammography interpretation) and cardiology, where they enhance diagnostic accuracy and improve patient outcomes. Looking ahead, chat and voice interfaces powered by large language models (LLMs) could support shared decision-making (SDM) by fostering better patient engagement and understanding.To fully realize the potential of AI-CDSS in advancing efficient, patient-centered care, it is essential to ensure their responsible development. This includes grounding AI models in domain-specific data, anonymizing user inputs, and implementing rigorous validation of AI-generated outputs before presentation. Thoughtful design and ethical oversight will be critical to integrating AI safely and effectively into clinical practice.