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Artificial intelligence (AI) in healthcare has the potential to improve patient outcomes, but clinician acceptance remains a critical barrier.
Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning
Xuefeng Peng, Yi Ding, David Wihl, Omer Gottesman, Matthieu Komorowski, Li Wei H. Lehman, Andrew Ross, Aldo Faisal, and Finale Doshi-Velez. 2018 · 1901
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Human-centered tools for coping with imperfect algorithms during medical decision-making
Carrie J. Cai, Emily Reif, Narayan Hegde, Jason Hipp, Been Kim, Daniel Smilkov, Martin Wattenberg, Fernanda Viegas, Greg S. Corrado, Martin C. Stumpe, and Michael Terry. 2019a · 1902
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Russell Jeter, Christopher Josef, Supreeth Shashikumar, and Shamim Nemati. 2019 · 1902
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Matthieu Komorowski, Leo A. Celi, Omar Badawi, Anthony C. Gordon, and A. Aldo Faisal. 2018 · 1902
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Qian Yang, Aaron Steinfeld, and John Zimmerman. 2019 · 1904
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Explainable reinforcement learning through a causal lens
Prashan Madumal, Tim Miller, Liz Sonenberg, and Frank Vetere. 2020 · 1905
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What clinicians want: Contextualizing explainable machine learning for clinical end use
Sana Tonekaboni, Shalmali Joshi, Melissa D. McCradden, and Anna Goldenberg. 2019 · 1905
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Reinforcement Learning in Healthcare: A Survey
Chao Yu, Jiming Liu, Shamim Nemati, and Guosheng Yin. 2023 · 1908
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“The human body is a black box”: Supporting clinical decision-making with deep learning
Mark Sendak, Madeleine Clare Elish, Michael Gao, Joseph Futoma, William Ratliff, Marshall Nichols, Armando Bedoya, Suresh Balu, and Cara O’Brien. 2020a · 1911
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Variations in Physician Practice: The Role of Uncertainty
David M. Eddy. 1984 · 1984
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Clinical decision making: from theory to practice. Anatomy of a decision
David M Eddy. 1990 · 1990
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Understanding why we agree on the evidence but disagree on the medicine
Gordon D Rubenfeld. 2001 · 2001
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Yunfeng Zhang, Q. Vera Liao, and Rachel K.E. Bellamy. 2020 · 2001
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Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs
Sungsoo Ray Hong, Jessica Hullman, and Enrico Bertini. 2020 · 2004
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Explainable Reinforcement Learning: A Survey
Erika Puiutta and Eric M.S.P. Veith. 2020 · 2005
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Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance
Gagan Bansal, Tongshuang Wu, Joyce Zhou, F. O.K. Raymond, Besmira Nushi, Ece Kamar, Marco Tulio Ribeiro, and Daniel S. Weld. 2020 · 2006
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Aniek F. Markus, Jan A. Kors, and Peter R. Rijnbeek. 2021 · 2007
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Reliable Post hoc Explanations: Modeling Uncertainty in Explainability
Dylan Slack, Sophie Hilgard, Sameer Singh, and Himabindu Lakkaraju. 2021 · 2008
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Sepsis bundles and compliance with clinical guidelines
Lisa Stoneking, Kurt Denninghoff, Lawrence DeLuca, Samuel M. Keim, and Benson Munger. 2011 · 2011
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An appraisal of the evidence underlying performance measures for community-acquired pneumonia
Kevin C Wilson and Holger J Schünemann. 2011 · 2011
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Too much, too little, or just right? Ways explanations impact end users’ mental models
Todd Kulesza, Simone Stumpf, Margaret Burnett, Sherry Yang, Irwin Kwan, and Weng Keen Wong. 2013 · 2013
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Two decades of mortality trends among patients with severe sepsis: a comparative meta-analysis
Elizabeth K Stevenson, Amanda R Rubenstein, Gregory T Radin, Renda Soylemez Wiener, and Allan J Walkey. 2014 · 2014
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The role of explanations on trust and reliance in clinical decision support systems
Adrian Bussone, Simone Stumpf, and Dympna O’Sullivan. 2015 · 2015
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XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (San Francisco, California, USA) (KDD ’16) . ACM, New York, NY, USA, 785–794
Tianqi Chen and Carlos Guestrin. 2016 · 2016
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Developing a New Definition and Assessing New Clinical Criteria for Septic Shock
Manu Shankar-Hari, Gary S. Phillips, Mitchell L. Levy, Christopher W. Seymour, Vincent X. Liu, Clifford S. Deutschman, Derek C. Angus, Gordon D. Rubenfeld, and Mervyn Singer. 2016 · 2016
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How good is the evidence to support primary care practice?
Mark H Ebell, Randi Sokol, Aaron Lee, Christopher Simons, and Jessica Early. 2017 · 2017
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State sepsis mandates-a new era for regulation of hospital quality
Tina B Hershey and Jeremy M Kahn. 2017 · 2017
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A unified approach to interpreting model predictions
Scott M. Lundberg and Su In Lee. 2017 · 2017
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Sepsis and septic shock
Maurizio Cecconi, Laura Evans, Mitchell Levy, and Andrew Rhodes. 2018 · 2018
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Clinicians’ cognitive biases: A potential barrier to implementation of evidence-based clinical practice
Claudia Caroline Dobler, Allison S. Morrow, and Celia C. Kamath. 2019 · 2018
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Learning to Treat Sepsis with Multi-Output Gaussian Process Deep Recurrent Q-Networks
Joseph Futoma, Anthony Lin, Mark Sendak, Armando Bedoya, Meredith Clement, Cara O’Brien, and Katherine Heller. 2018 · 2018
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ClinicalVis: Supporting Clinical Task-Focused Design Evaluation
Marzyeh Ghassemi, Mahima Pushkarna, James Wexler, Jesse Johnson, and Paul Varghese. 2018 · 2018
Cited alongside, same era.
Fluid resuscitation during early sepsis: A need for individualization
Mathieu Jozwiak, Olfa Hamzaoui, Xavier Monnet, and Jean Louis Teboul. 2018 · 2018
Cited alongside, same era.
Reasons for physicians not adopting clinical decision support systems: Critical analysis
Saif Khairat, David Marc, William Crosby, and Ali Al Sanousi. 2018 · 2018
Cited alongside, same era.
Updates in human-ai teams: Understanding and addressing the performance/compatibility tradeoff
Gagan Bansal, Besmira Nushi, Ece Kamar, Daniel S. Weld, Walter S. Lasecki, and Eric Horvitz. 2019 · 2019
Cited alongside, same era.
National performance on the Medicare SEP-1 sepsis quality measure
Ian J Barbash, Billie Davis, and Jeremy M Kahn. 2019 · 2019
Cited alongside, same era.
Introduction of human-centric AI assistant to aid radiologists for multimodal breast image classification
Francisco Maria Calisto, Carlos Santiago, Nuno Nunes, and Jacinto C. Nascimento. 2021 · 2021
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What is sepsis?
Centers for Disease Control and Prevention. 2021 · 2021
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I Think I Get Your Point, AI! The Illusion of Explanatory Depth in Explainable AI
Michael Chromik, Malin Eiband, Felicitas Buchner, Adrian Krüger, and Andreas Butz. 2021 · 2021
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The Who in Explainable AI: How AI Background Shapes Perceptions of AI Explanations
Upol Ehsan, Samir Passi, Q. Vera Liao, Larry Chan, I-Hsiang Lee, Michael Muller, and Mark O. Riedl. 2021a · 2021
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Operationalizing Human-Centered Perspectives in Explainable AI
Upol Ehsan, Philipp Wintersberger, Q. Vera Liao, Martina Mara, Marc Streit, Sandra Wachter, Andreas Riener, and Mark O. Riedl. 2021b · 2021
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Clinical decision support for therapeutic decision-making in cancer: A systematic review
Melissa Beauchemin, Meghan T. Murray, Lillian Sung, Dawn L. Hershman, Chunhua Weng, and Rebecca Schnall. 2019 · 2019
Cited alongside, same era.
“Hello AI”: Uncovering the onboarding needs of medical practitioners for human–AI collaborative decision-making
Carrie J. Cai, Samantha Winter, David Steiner, Lauren Wilcox, and Michael Terry. 2019b · 2019
Cited alongside, same era.
Clinician Perception of a Machine Learning-Based Early Warning System Designed to Predict Severe Sepsis and Septic Shock
Jennifer C. Ginestra, Heather M. Giannini, William D. Schweickert, Laurie Meadows, Michael J. Lynch, Kimberly Pavan, Corey J. Chivers, Michael Draugelis, Patrick J. Donnelly, Barry D. Fuchs, and Craig A. Umscheid. 2019 · 2019
Cited alongside, same era.
CDS in a learning health care system: Identifying physicians’ reasons for rejection of best-practice recommendations in pneumonia through computerized clinical decision support
Barbara E. Jones, Dave S. Collingridge, Caroline G. Vines, Herman Post, John Holmen, Todd L. Allen, Peter Haug, Charlene R. Weir, and Nathan C. Dean. 2019 · 2019
Cited alongside, same era.
The “inconvenient truth” about AI in healthcare
Trishan Panch, Heather Mattie, and Leo Anthony Celi. 2019 · 2019
Cited alongside, same era.
What AI means for doctors and doctoring
Nirav R Shah and Thomas H Lee. 2019 · 2019
Cited alongside, same era.
High-performance medicine: the convergence of human and artificial intelligence
Eric J. Topol. 2019 · 2019
Cited alongside, same era.
Deep learning-enabled medical computer vision
Andre Esteva, Katherine Chou, Serena Yeung, Nikhil Naik, Ali Madani, Ali Mottaghi, Yun Liu, Eric Topol, Jeff Dean, and Richard Socher. 2021 · 2021
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Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock 2021 . Vol. 49
Laura Evans, Andrew Rhodes, Waleed Alhazzani, Massimo Antonelli, Craig M. Coopersmith, Craig French, Flávia R. MacHado, Lauralyn McIntyre, Marlies Ostermann, Hallie C. Prescott, Christa Schorr, Steven Simpson, W. Joost Wiersinga, Fayez Alshamsi, Derek C. Angus, Yaseen Arabi, Luciano Azevedo, Richard Beale, Gregory Beilman, Emilie Belley-Cote, Lisa Burry, Maurizio Cecconi, John Centofanti, Angel Coz Yataco, Jan De Waele, R. Phillip Dellinger, Kent Doi, Bin Du, Elisa Estenssoro, Ricard Ferrer, Charles Gomersall, Carol Hodgson, Morten Hylander Møller, Theodore Iwashyna, Shevin Jacob, Ruth Kleinpell, Michael Klompas, Younsuck Koh, Anand Kumar, Arthur Kwizera, Suzana Lobo, Henry Masur, Steven McGloughlin, Sangeeta Mehta, Yatin Mehta, Mervyn Mer, Mark Nunnally, Simon Oczkowski, Tiffany Osborn, Elizabeth Papathanassoglou, Anders Perner, Michael Puskarich, Jason Roberts, William Schweickert, Maureen Seckel, Jonathan Sevransky, Charles L. Sprung, Tobias Welte, Janice Zimmerman, and Mitchell Levy. 2021 · 2021
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Do as AI say: susceptibility in deployment of clinical decision-aids
Susanne Gaube, Harini Suresh, Martina Raue, Alexander Merritt, Seth J. Berkowitz, Eva Lermer, Joseph F. Coughlin, John V. Guttag, Errol Colak, and Marzyeh Ghassemi. 2021 · 2021
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The false hope of current approaches to explainable artificial intelligence in health care
Marzyeh Ghassemi, Luke Oakden-Rayner, and Andrew L. Beam. 2021 · 2021
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Explainable Artificial Intelligence Approaches: A Survey
Sheikh Rabiul Islam, William Eberle, Sheikh Khaled Ghafoor, and Mohiuddin Ahmed. 2021 · 2021
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How machine-learning recommendations influence clinician treatment selections: the example of the antidepressant selection
Maia Jacobs, Melanie F. Pradier, Thomas H. McCoy, Roy H. Perlis, Finale Doshi-Velez, and Krzysztof Z. Gajos. 2021 · 2021
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Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies
Vivian Lai, Chacha Chen, Q. Vera Liao, Alison Smith-Renner, and Chenhao Tan. 2021 · 2021
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A human-ai collaborative approach for clinical decision making on rehabilitation assessment. In Conference on Human Factors in Computing Systems - Proceedings . Association for Computing Machinery
Min Hun Lee, Daniel P. Siewiorek, and Asim Smailagic. 2021 · 2021
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Early prediction of sepsis in the ICU using machine learning: a systematic review
Michael Moor, Bastian Rieck, Max Horn, Catherine R Jutzeler, and Karsten Borgwardt. 2021 · 2021
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Human-centered XAI: Developing design patterns for explanations of clinical decision support systems
Tjeerd A.J. Schoonderwoerd, Wiard Jorritsma, Mark A. Neerincx, and Karel van den Bosch. 2021 · 2021
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Harini Suresh, Steven R. Gomez, Kevin K. Nam, and Arvind Satyanarayan. 2021 · 2021
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Brilliant ai doctor in rural clinics: Challenges in AI-powered clinical decision support system deployment
Dakuo Wang, Liuping Wang, and Zhan Zhang. 2021 · 2021
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Alexandra Zytek, Dongyu Liu, Rhema Vaithianathan, and Kalyan Veeramachaneni. 2021 · 2021
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To explain or not to explain?—Artificial intelligence explainability in clinical decision support systems
Julia Amann, Dennis Vetter, Stig Nikolaj Blomberg, Helle Collatz Christensen, Megan Coffee, Sara Gerke, Thomas K. Gilbert, Thilo Hagendorff, Sune Holm, Michelle Livne, Andy Spezzatti, Inga Strümke, Roberto V. Zicari, and Vince Istvan Madai. 2022 · 2022
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Re-focusing explainability in medicine
Laura Arbelaez Ossa, Georg Starke, Giorgia Lorenzini, Julia E. Vogt, David M. Shaw, and Bernice Simone Elger. 2022 · 2022
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Modeling adoption of intelligent agents in medical imaging
Francisco Maria Calisto, Nuno Nunes, and Jacinto C. Nascimento. 2022 · 2022
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Human – machine teaming is key to AI adoption : clinicians ’ experiences with a deployed machine learning system
Katharine E Henry, Rachel Korn, Anirudh Sridharan, Robert C Linton, Catherine Groh, Tony Wang, and Albert Wu. 2022 · 2022
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Anna Kawakami, Venkatesh Sivaraman, Hao-Fei Cheng, Logan Stapleton, Yanghuidi Cheng, Diana Qing, Adam Perer, Zhiwei Steven Wu, Haiyi Zhu, and Kenneth Holstein. 2022 · 2022
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The Impact of Choice Architecture on Sepsis Fluid Resuscitation Decisions: An Exploratory Survey-Based Study
Jason N Mansoori, Brendan J Clark, Edward P Havranek, and Ivor S Douglas. 2022 · 2022
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Generalizing Off-Policy Evaluation From a Causal Perspective For Sequential Decision-Making
Sonali Parbhoo, Shalmali Joshi, and Finale Doshi-Velez. 2022 · 2022
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Fusion of fully integrated analog machine learning classifier with electronic medical records for real-time prediction of sepsis onset
Sudarsan Sadasivuni, Monjoy Saha, Neal Bhatia, Imon Banerjee, and Arindam Sanyal. 2022 · 2022
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Trust and Reliance in XAI – Distinguishing Between Attitudinal and Behavioral Measures
Nicolas Scharowski, Sebastian A. C. Perrig, Nick von Felten, and Florian Brühlmann. 2022 · 2022
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Clinical Decision Support Software
U.S. Food and Drug Administration. 2022 · 2022
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Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI
Baptiste Vasey, Myura Nagendran, Bruce Campbell, David A. Clifton, Gary S. Collins, Spiros Denaxas, Alastair K. Denniston, Livia Faes, Bart Geerts, Mudathir Ibrahim, Xiaoxuan Liu, Bilal A. Mateen, Piyush Mathur, Melissa D. McCradden, Lauren Morgan, Johan Ordish, Campbell Rogers, Suchi Saria, Daniel S. W. Ting, Peter Watkinson, Wim Weber, Peter Wheatstone, Peter McCulloch, and DECIDE-AI Expert Group. 2022 · 2022
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Randomized Trial of Automated, Electronic Monitoring to Facilitate Early Detection of Sepsis in the Intensive Care Unit Michael
Michael H Hooper, Lisa Weavind, Arthur P Wheeler, Supriya Srinivasa Gowda, Matthew W Semler, Rachel M Hayes, Daniel W Albert, Norment B Deane, Hui Nian, Janos L Mathe, Andras Nadas, Janos Sztipanovits, Anne Miller, and Todd W Rice. 2015 · 2096
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