Experimental evidence of effective human–AI collaboration in medical decision-making

Abstract

Artificial Intelligence (ai) systems are precious support for decision-making, with many applications also in the medical domain. The interaction between mds and ai enjoys a renewed interest following the increased possibilities of deep learning devices. However, we still have limited evidence-based knowledge of the context, design, and psychological mechanisms that craft an optimal human–ai collaboration. In this multicentric study, 21 endoscopists reviewed 504 videos of lesions prospectively acquired from real colonoscopies. They were asked to provide an optical diagnosis with and without the assistance of an ai support system. Endoscopists were influenced by ai ($$\textsc {or}=3.05$$ O R = 3.05 ), but not erratically: they followed the ai advice more when it was correct ($$\textsc {or}=3.48$$ O R = 3.48 ) than incorrect ($$\textsc {or}=1.85$$ O R = 1.85 ). Endoscopists achieved this outcome through a weighted integration of their and the ai opinions, considering the case-by-case estimations of the two reliabilities. This Bayesian-like rational behavior allowed the human–ai hybrid team to outperform both agents taken alone. We discuss the features of the human–ai interaction that determined this favorable outcome.

A Review of the Technology, Training, and Assessment Methods for the First Real-Time AI-Enhanced Medical Device for Endoscopy

“…In contrast, when a “no-prediction” overlay is shown for a lesion, the endoscopist will correctly interpret the AI to be less confident. According to the findings of Reverberi et al [ 20 ], the CADx’s ability to influence the endoscopists’ decisions was crucial, as they tended to rely more on the AI opinion when they lacked confidence and when the perceived AI confidence was high. Conversely, when the endoscopists were confident and the perceived AI confidence was low, they were more likely to stick with their diagnoses.…”

“…In 2021, in addition to the already existing CADe module, a CADx module was added to GI Genius version 3 (v3), which received CE clearance under the European Medical Device Directive (MDD, 93/42/EEC) as a class IIa medical device. GI Genius CADx was utilized in a series of studies that assessed the performances of its novel feature for polyp characterization [ 18 , 19 , 20 ].…”

“…A third experiment was conducted to study the effects of the CADx output on an endoscopists’ optical characterization [ 20 ]. The endoscopists were asked to characterize a set of video-recorded polyps without an AI overlay in this experiment.…”

“…In this work, we provide an overview of the technology and scientific evidence behind GI Genius, the first AI-empowered medical device for colonoscopy to enter the endoscopy market [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. In the following sections, we briefly describe the technical architecture of the hardware and software configuration, the AI algorithm training and testing strategies, and an update of the regulatory path.…”

Current and future implications of artificial intelligence in colonoscopy

“…To explore the interaction between CADx and human endoscopists, a recent study [ 37 ] used a novel design, showing a team of both expert and non-expert endoscopists the same (reshuffled) set of colorectal diminutive lesions, first without the AI overlay and second with the AI overlay. The aim was to analyze how the decision of the endoscopist is influenced by the availability of the AI optical diagnosis output.…”