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Papers on “explainable AI clinical decision support”

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1. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

   Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser, et al. · 2019 · Information Fusion · 9,973 cites

2. A survey of methods for explaining black box models

   Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, et al. · 2019 · ISTI Open Portal · 5,046 cites

   In recent years, many accurate decision support systems have been constructed as black boxes, that is as systems that hide their internal logic to the user. This lack of explanation constitutes both a practical and an ethical issue. The literature reports many approaches aimed at overcoming this crucial weakness, sometimes at the cost of sacrificing accuracy for interpretability. The applications in which black box decision systems can be used are various, and each approach is typically developed to provide a solution for a specific problem and, as a consequence, it explicitly or implicitly delineates its own definition of interpretability and explanation. The aim of this article is to provi

3. Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models

   Tiffany H. Kung, Morgan Cheatham, Arielle Medenilla, et al. · 2023 · PLOS Digital Health · 3,847 cites

   We evaluated the performance of a large language model called ChatGPT on the United States Medical Licensing Exam (USMLE), which consists of three exams: Step 1, Step 2CK, and Step 3. ChatGPT performed at or near the passing threshold for all three exams without any specialized training or reinforcement. Additionally, ChatGPT demonstrated a high level of concordance and insight in its explanations. These results suggest that large language models may have the potential to assist with medical education, and potentially, clinical decision-making.

4. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods

   Professor Gary S. Collins, Karel G.M. Moons, Paula Dhiman, et al. · 2024 · BMJ · 3,508 cites

   The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) statement was published in 2015 to provide the minimum reporting recommendations for studies developing or evaluating the performance of a prediction model. Methodological advances in the field of prediction have since included the widespread use of artificial intelligence (AI) powered by machine learning methods to develop prediction models. An update to the TRIPOD statement is thus needed. TRIPOD+AI provides harmonised guidance for reporting prediction model studies, irrespective of whether regression modelling or machine learning methods have been used. The new checklist supersedes

5. An overview of clinical decision support systems: benefits, risks, and strategies for success

   Reed T. Sutton, David Pincock, Daniel C. Baumgart, et al. · 2020 · npj Digital Medicine · 3,078 cites

   Computerized clinical decision support systems, or CDSS, represent a paradigm shift in healthcare today. CDSS are used to augment clinicians in their complex decision-making processes. Since their first use in the 1980s, CDSS have seen a rapid evolution. They are now commonly administered through electronic medical records and other computerized clinical workflows, which has been facilitated by increasing global adoption of electronic medical records with advanced capabilities. Despite these advances, there remain unknowns regarding the effect CDSS have on the providers who use them, patient outcomes, and costs. There have been numerous published examples in the past decade(s) of CDSS succes

6. Explainability for artificial intelligence in healthcare: a multidisciplinary perspective

   Julia Amann, Alessandro Blasimme, Effy Vayena, et al. · 2020 · BMC Medical Informatics and Decision Making · 2,154 cites

   BACKGROUND: Explainability is one of the most heavily debated topics when it comes to the application of artificial intelligence (AI) in healthcare. Even though AI-driven systems have been shown to outperform humans in certain analytical tasks, the lack of explainability continues to spark criticism. Yet, explainability is not a purely technological issue, instead it invokes a host of medical, legal, ethical, and societal questions that require thorough exploration. This paper provides a comprehensive assessment of the role of explainability in medical AI and makes an ethical evaluation of what explainability means for the adoption of AI-driven tools into clinical practice. METHODS: Taking A

7. The false hope of current approaches to explainable artificial intelligence in health care

   Marzyeh Ghassemi, Luke Oakden‐Rayner, Andrew L. Beam · 2021 · The Lancet Digital Health · 1,635 cites

   The black-box nature of current artificial intelligence (AI) has caused some to question whether AI must be explainable to be used in high-stakes scenarios such as medicine. It has been argued that explainable AI will engender trust with the health-care workforce, provide transparency into the AI decision making process, and potentially mitigate various kinds of bias. In this Viewpoint, we argue that this argument represents a false hope for explainable AI and that current explainability methods are unlikely to achieve these goals for patient-level decision support. We provide an overview of current explainability techniques and highlight how various failure cases can cause problems for deci

8. Designing Theory-Driven User-Centric Explainable AI

   Danding Wang, Qian Yang, Ashraf Abdul, et al. · 2019 · 878 cites

   From healthcare to criminal justice, artificial intelligence (AI) is increasingly supporting high-consequence human decisions. This has spurred the field of explainable AI (XAI). This paper seeks to strengthen empirical application-specific investigations of XAI by exploring theoretical underpinnings of human decision making, drawing from the fields of philosophy and psychology. In this paper, we propose a conceptual framework for building human-centered, decision-theory-driven XAI based on an extensive review across these fields. Drawing on this framework, we identify pathways along which human cognitive patterns drives needs for building XAI and how XAI can mitigate common cognitive biases