Diagnostic errors impact patient health and healthcare costs. Artificial Intelligence (AI) shows promise in mitigating this burden by supporting Medical Doctors in decision-making. However, the mere display of excellent or even superhuman performance by AI in specific tasks does not guarantee a positive impact on medical practice. Effective AI assistance should target the primary causes of human errors and foster effective collaborative decision-making with human experts who remain the ultimate decision-makers. In this narrative review, we apply these principles to the specific scenario of AI assistance during colonoscopy. By unraveling the neurocognitive foundations of the colonoscopy procedure, we identify multiple bottlenecks in perception, attention, and decision-making that contribute to diagnostic errors, shedding light on potential interventions to mitigate them. Furthermore, we explored how existing AI devices fare in clinical practice and whether they achieved an optimal integration with the human decision-maker. We argue that to foster optimal Human-AI collaboration, future research should expand our knowledge of factors influencing AI's impact, establish evidence-based cognitive models, and develop training programs based on them. These efforts will enhance human-AI collaboration, ultimately improving diagnostic accuracy and patient outcomes. The principles illuminated in this review hold more general value, extending their relevance to a wide array of medical procedures and beyond.

Introzzi, L., Zonca, J., Cabitza, F., Cherubini, P., Reverberi, C. (2023). Enhancing human-AI collaboration: The case of colonoscopy. DIGESTIVE AND LIVER DISEASE [10.1016/j.dld.2023.10.018].

Enhancing human-AI collaboration: The case of colonoscopy

Introzzi L.
Primo
;
Zonca J.
Secondo
;
Cabitza F.;Cherubini P.;Reverberi C.
Ultimo
2023

Abstract

Diagnostic errors impact patient health and healthcare costs. Artificial Intelligence (AI) shows promise in mitigating this burden by supporting Medical Doctors in decision-making. However, the mere display of excellent or even superhuman performance by AI in specific tasks does not guarantee a positive impact on medical practice. Effective AI assistance should target the primary causes of human errors and foster effective collaborative decision-making with human experts who remain the ultimate decision-makers. In this narrative review, we apply these principles to the specific scenario of AI assistance during colonoscopy. By unraveling the neurocognitive foundations of the colonoscopy procedure, we identify multiple bottlenecks in perception, attention, and decision-making that contribute to diagnostic errors, shedding light on potential interventions to mitigate them. Furthermore, we explored how existing AI devices fare in clinical practice and whether they achieved an optimal integration with the human decision-maker. We argue that to foster optimal Human-AI collaboration, future research should expand our knowledge of factors influencing AI's impact, establish evidence-based cognitive models, and develop training programs based on them. These efforts will enhance human-AI collaboration, ultimately improving diagnostic accuracy and patient outcomes. The principles illuminated in this review hold more general value, extending their relevance to a wide array of medical procedures and beyond.
Articolo in rivista - Articolo scientifico
Artificial intelligence; Cognitive bias; Cognitive bottlenecks; Diagnostic errors; Endoscopy; Human - AI collaboration; Hybrid intelligence;
English
6-nov-2023
2023
open
Introzzi, L., Zonca, J., Cabitza, F., Cherubini, P., Reverberi, C. (2023). Enhancing human-AI collaboration: The case of colonoscopy. DIGESTIVE AND LIVER DISEASE [10.1016/j.dld.2023.10.018].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/453219
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