"Hello AI": Uncovering the Onboarding Needs of Medical Practitioners for Human-AI Collaborative Decision-Making
Carrie J. Cai,Samantha Winter,David F. Steiner,Lauren Wilcox,Michael Terry +4 more
- Vol. 3, pp 104
TLDR
This work investigates the key types of information medical experts desire when they are first introduced to a diagnostic AI assistant, providing a richer understanding of what experts find important in their introduction to AI assistants before integrating them into routine practice.Abstract:
Although rapid advances in machine learning have made it increasingly applicable to expert decision-making, the delivery of accurate algorithmic predictions alone is insufficient for effective human-AI collaboration. In this work, we investigate the key types of information medical experts desire when they are first introduced to a diagnostic AI assistant. In a qualitative lab study, we interviewed 21 pathologists before, during, and after being presented deep neural network (DNN) predictions for prostate cancer diagnosis, to learn the types of information that they desired about the AI assistant. Our findings reveal that, far beyond understanding the local, case-specific reasoning behind any model decision, clinicians desired upfront information about basic, global properties of the model, such as its known strengths and limitations, its subjective point-of-view, and its overall design objective--what it's designed to be optimized for. Participants compared these information needs to the collaborative mental models they develop of their medical colleagues when seeking a second opinion: the medical perspectives and standards that those colleagues embody, and the compatibility of those perspectives with their own diagnostic patterns. These findings broaden and enrich discussions surrounding AI transparency for collaborative decision-making, providing a richer understanding of what experts find important in their introduction to AI assistants before integrating them into routine practice.read more
Citations
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Journal ArticleDOI
Human–computer collaboration for skin cancer recognition
Philipp Tschandl,Christoph Rinner,Zoe Apalla,Giuseppe Argenziano,Noel C. F. Codella,Allan C. Halpern,Monika Janda,Aimilios Lallas,Caterina Longo,Josep Malvehy,Josep Malvehy,John Paoli,John Paoli,Susana Puig,Susana Puig,Cliff Rosendahl,H. Peter Soyer,Iris Zalaudek,Harald Kittler +18 more
TL;DR: A systematic evaluation of the value of AI-based decision support in skin tumor diagnosis demonstrates the superiority of human–computer collaboration over each individual approach and supports the potential of automated approaches in diagnostic medicine.
Proceedings ArticleDOI
Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning
Harmanpreet Kaur,Harsha Nori,Samuel Jenkins,Rich Caruana,Hanna Wallach,Jennifer Wortman Vaughan +5 more
TL;DR: It is indicated that data scientists over-trust and misuse interpretability tools, and few of their participants were able to accurately describe the visualizations output by these tools.
Proceedings ArticleDOI
A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy
Emma Beede,Elizabeth Elliott Baylor,Fred Hersch,Anna Iurchenko,Lauren Wilcox,Paisan Ruamviboonsuk,Laura Vardoulakis +6 more
TL;DR: A human-centered study of a deep learning system used in clinics for the detection of diabetic eye disease in Thailand indicates that several socio-environmental factors impact model performance, nursing workflows, and the patient experience.
Journal ArticleDOI
How Machine Learning Will Transform Biomedicine
TL;DR: A vision for how machine learning can transform three broad areas of biomedicine: clinical diagnostics, precision treatments, and health monitoring, where the goal is to maintain health through a range of diseases and the normal aging process is outlined.
Posted Content
Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance
Gagan Bansal,Tongshuang Wu,Joyce Zhou,Raymond Fok,Besmira Nushi,Ece Kamar,Marco Tulio Ribeiro,Daniel S. Weld +7 more
TL;DR: This work conducts mixed-method user studies on three datasets, where an AI with accuracy comparable to humans helps participants solve a task (explaining itself in some conditions), and observes complementary improvements from AI augmentation that were not increased by explanations.
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