Human-computer collaboration for skin cancer recognition.
Τίτλος | Human-computer collaboration for skin cancer recognition. |
Publication Type | Journal Article |
Year of Publication | 2020 |
Authors | Tschandl, P., Rinner C., Apalla Z., Argenziano G., Codella N., Halpern A., Janda M., Lallas A., Longo C., Malvehy J., Paoli J., Puig S., Rosendahl C., H Soyer P., Zalaudek I., & Kittler H. |
Journal | Nat Med |
Volume | 26 |
Issue | 8 |
Pagination | 1229-1234 |
Date Published | 2020 08 |
ISSN | 1546-170X |
Λέξεις κλειδιά | Artificial Intelligence, Clinical Decision-Making, Humans, Neural Networks, Computer, Physicians, Skin Neoplasms, Telemedicine, User-Computer Interface |
Abstract | The rapid increase in telemedicine coupled with recent advances in diagnostic artificial intelligence (AI) create the imperative to consider the opportunities and risks of inserting AI-based support into new paradigms of care. Here we build on recent achievements in the accuracy of image-based AI for skin cancer diagnosis to address the effects of varied representations of AI-based support across different levels of clinical expertise and multiple clinical workflows. We find that good quality AI-based support of clinical decision-making improves diagnostic accuracy over that of either AI or physicians alone, and that the least experienced clinicians gain the most from AI-based support. We further find that AI-based multiclass probabilities outperformed content-based image retrieval (CBIR) representations of AI in the mobile technology environment, and AI-based support had utility in simulations of second opinions and of telemedicine triage. In addition to demonstrating the potential benefits associated with good quality AI in the hands of non-expert clinicians, we find that faulty AI can mislead the entire spectrum of clinicians, including experts. Lastly, we show that insights derived from AI class-activation maps can inform improvements in human diagnosis. Together, our approach and findings offer a framework for future studies across the spectrum of image-based diagnostics to improve human-computer collaboration in clinical practice. |
DOI | 10.1038/s41591-020-0942-0 |
Alternate Journal | Nat Med |
PubMed ID | 32572267 |