Clinical Decision Support in the Era of Artificial Intelligence.
By Edward H. Shortliffe, M. Sepúlveda. Journal of the American Medical Association (JAMA), 2018
753 citations recorded by the source corpus.
Subfields: Health Informatics, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/74a6826bcd97afbc7bcb175d622ab63f1596d260
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In Health Informatics
In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- Artificial Intelligence and Surgical Decision-Making (2019, methodology): “74 In cooperation with the International Medical Device Regulators Forum, the US Food and Drug Administration created the Software as Medical Device category and developed a voluntary Software Precertification Program to aid health care software developers in creating, testing, and implementing…”
- Artificial intelligence in nursing: Priorities and opportunities from an international invitational think-tank of the Nursing and Artificial Intelligence Leadership Collaborative (2021, background): “Also, AI-based clinical decision support has various levels of uncertainty that requires clinician interpretation (Shortliffe & Sepulveda, 2018).”
- Introduction to artificial intelligence in medicine (2019, background): “The system should also be user-friendly and intuitive with no major training required to use or analyse the results [34] Additionally, it is of importance to remember that these systems are support systems and not meant to replace physicians or their knowledge, but rather to augment it.”
- Design Characteristics of Studies Reporting the Performance of Artificial Intelligence Algorithms for Diagnostic Analysis of Medical Images: Results from Recently Published Papers (2019, background): “As with any other medical devices or technologies, the importance of thorough clinical validation of AI algorithms before their adoption in clinical practice through adequately designed studies to ensure patient benefit and safety while avoiding any inadvertent harms cannot be overstated (5-10).”
- Evaluation and mitigation of the limitations of large language models in clinical decision-making (2024, neutral): “We believe that there exists great potential in using LLMs as clinical support systems with close collaboration between models and clinicians 21,27 ; however, we have primarily evaluated the capabilities of models as autonomous decision-makers in this study.”
- Explainable AI for Trees: From Local Explanations to Global Understanding (2019, background): “It is particularly important in medical applications, where the patterns uncovered by a model are often even more important than the model’s prediction performance [64, 42].”