Visual explanations for polyp detection: How medical doctors assess intrinsic versus extrinsic explanations

• DOI: 10.1371/journal.pone.0304069
• Publication Date: 2024-05-31T18:57:03Z
• Authors: Steven Hicks; Andrea Storås; Michael A. Riegler; Cise Midoglu; Malek Hammou; Thomas de Lange; Sravanthi Parasa; Pål Halvorsen; Inga Strümke
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; Computer Science - Artificial Intelligence; Science; Computer Vision and Pattern Recognition (cs.CV); Computer Science - Computer Vision and Pattern Recognition; Colonic Polyps; Machine Learning (cs.LG); 03 medical and health sciences; Deep Learning; 0302 clinical medicine; Artificial Intelligence; Physicians; FOS: Electrical engineering, electronic engineering, information engineering; Humans; Q; Image and Video Processing (eess.IV); R; Colonoscopy; Electrical Engineering and Systems Science - Image and Video Processing; 3. Good health; Artificial Intelligence (cs.AI); Medicine; Neural Networks, Computer; Research Article

Deep learning has achieved immense success in computer vision and has the potential to help physicians analyze visual content for disease and other abnormalities. However, the current state of deep learning is very much a black box, making medical professionals skeptical about integrating these methods into clinical practice. Several methods have been proposed to shed some light on these black boxes, but there is no consensus on the opinion of medical doctors that will consume these explanations. This paper presents a study asking medical professionals about their opinion of current state-of-the-art explainable artificial intelligence methods when applied to a gastrointestinal disease detection use case. We compare two different categories of explanation methods, intrinsic and extrinsic, and gauge their opinion of the current value of these explanations. The results indicate that intrinsic explanations are preferred and that physicians see value in the explanations. Based on the feedback collected in our study, future explanations of medical deep neural networks can be tailored to the needs and expectations of doctors. Hopefully, this will contribute to solving the issue of black box medical systems and lead to successful implementation of this powerful technology in the clinic.