David Faust, PhD is Professor, Department of Psychology and Fellow of the Ryan Institute of Neuroscience, University of Rhode Island; Affiliate Professor, Warren Alpert Medical School of Brown University, Department of Psychiatry and Human BehaviorHal R. Arkes, PhD is Emeritus Professor of Psychology, Ohio State University; Associate, Harding Center for Risk LiteracyCharles E. Gaudet, PhD is Fellow, Department of Physical Medicine and Rehabilitation, Harvard Medical School, Massachusetts General Hospital, Spaulding Rehabilitation Hospital

• Part I: Introduction and Foundations Chapter 1 The Benefits of Decision Research for Clinical Practice: Framing the Issues Chapter 2 Avoiding Unnecessary Error Part II: Gathering, Appraising, and Integrating Information Chapter 3 Assessing Whether Clinical Variables Are Related: The Important Task of Covariation Estimation Chapter 4 Confirmation Bias Chapter 5 Impediments to Accurate Decision Making Chapter 6 Base Rates: One of the Diagnostician's and Prognosticator's Greatest Allies Chapter 7 The Integration of Information: Strengths and Limits Chapter 8 Comparing Clinical Judgment and Statistical Decision Methods Chapter 9 Research on Racial/Ethnic Biases and Professional Judgment: Errors in Cold Cognition, Hot Cognition, and Interconnections with Decision Research Chapter 10 The Use of Artificial Intelligence in Clinical Decision Making: Current Status, Potential Advantages and Limits, Future Prospects Part III: Corrective Methods and Strategies Chapter 11 Overconfidence and the Limits of Experience Chapter 12 The Limits of Insight Alone; Need for Active Steps, and Use of Debiasing Strategies Chapter 13 Approaches for Improving Information Gathering, Appraising the Utility of Information, and Combining and Interpreting Information Appendices Appendix 1 A sampling of base rate sources Appendix 2 Base rate nomogram Appendix 3 Examples of combining base rates with other diagnostic signs, indicators, and test results Appendix 4 A sampling of recent publications in five areas of machine learning and artificial intelligence related to applied clinical practice Appendix 5 Practice Cases and Clinical Scenarios Appendix 6 Checklists/Guidelines

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