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Introzzi L, Zonca J, Cabitza F, Cherubini P, Reverberi C. Enhancing human-AI collaboration: The case of colonoscopy. Dig Liver Dis 2024; 56:1131-1139. [PMID: 37940501 DOI: 10.1016/j.dld.2023.10.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 10/19/2023] [Accepted: 10/23/2023] [Indexed: 11/10/2023]
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.
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Affiliation(s)
- Luca Introzzi
- Department of Psychology, Università Milano - Bicocca, Milano, Italy
| | - Joshua Zonca
- Department of Psychology, Università Milano - Bicocca, Milano, Italy; Milan Center for Neuroscience, Università Milano - Bicocca, Milano, Italy
| | - Federico Cabitza
- Department of Informatics, Systems and Communication, Università Milano - Bicocca, Milano, Italy; IRCCS Istituto Ortopedico Galeazzi, Milano, Italy
| | - Paolo Cherubini
- Department of Brain and Behavioral Sciences, Università Statale di Pavia, Pavia, Italy
| | - Carlo Reverberi
- Department of Psychology, Università Milano - Bicocca, Milano, Italy; Milan Center for Neuroscience, Università Milano - Bicocca, Milano, Italy.
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Savino A, Rondonotti E, Rocchetto S, Piagnani A, Bina N, Di Domenico P, Segatta F, Radaelli F. GI genius endoscopy module: a clinical profile. Expert Rev Med Devices 2024; 21:359-372. [PMID: 38618982 DOI: 10.1080/17434440.2024.2342508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 04/09/2024] [Indexed: 04/16/2024]
Abstract
INTRODUCTION The identification of early-stage colorectal cancers (CRC) and the resection of pre-cancerous neoplastic lesions through colonoscopy allows to decrease both CRC incidence and mortality. However, colonoscopy miss rates up to 26% for adenomas and 9% for advanced adenomas have been reported. In recent years, artificial intelligence (AI) systems have been emerging as easy-to-use tools, potentially lowering the risk of missing lesions. AREAS COVERED This review paper focuses on GI Genius device (Medtronic Co. Minneapolis, MN, U.S.A.) a computer-assisted tool designed to assist endoscopists during standard white-light colonoscopies in detecting mucosal lesions. EXPERT OPINION Randomized controlled trials (RCTs) suggest that GI Genius is a safe and effective tool for improving adenoma detection, especially in CRC screening and surveillance colonoscopies. However, its impact seems to be less significant among experienced endoscopists and in real-world clinical scenarios compared to the controlled conditions of RCTs. Furthermore, it appears that GI Genius mainly enhances the detection of non-advanced, small polyps, but does not significantly impact the identification of advanced and difficult-to-detect adenoma. When using GI Genius, no complications were documented. Only a small number of studies reported an increased in withdrawal time or the removal of non-neoplastic lesions.
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Affiliation(s)
- Alberto Savino
- Division of Gastroenterology, Department of Medicine and Surgery, University of Milano-Bicocca, Milano, Italy
| | | | - Simone Rocchetto
- Gastroenterology Unit, Valduce Hospital, Como, Italy
- Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Department of Gastroenterology and Hepatology, University of Milan, Milan, Italy
| | - Alessandra Piagnani
- Gastroenterology Unit, Valduce Hospital, Como, Italy
- Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Department of Gastroenterology and Hepatology, University of Milan, Milan, Italy
| | - Niccolò Bina
- Gastroenterology Unit, Valduce Hospital, Como, Italy
| | - Pasquale Di Domenico
- Gastrointestinal Unit, Department of Medicine, Surgery & Dentistry Scuola Medica Salernitana, University of Salerno, Salerno, Italy
| | - Francesco Segatta
- Gastroenterology Unit, Valduce Hospital, Como, Italy
- Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Department of Gastroenterology and Hepatology, University of Milan, Milan, Italy
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Thiruvengadam NR, Solaimani P, Shrestha M, Buller S, Carson R, Reyes-Garcia B, Gnass RD, Wang B, Albasha N, Leonor P, Saumoy M, Coimbra R, Tabuenca A, Srikureja W, Serrao S. The Efficacy of Real-time Computer-aided Detection of Colonic Neoplasia in Community Practice: A Pragmatic Randomized Controlled Trial. Clin Gastroenterol Hepatol 2024:S1542-3565(24)00225-8. [PMID: 38437999 DOI: 10.1016/j.cgh.2024.02.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 02/12/2024] [Accepted: 02/13/2024] [Indexed: 03/06/2024]
Abstract
BACKGROUND & AIMS The use of computer-aided detection (CADe) has increased the adenoma detection rates (ADRs) during colorectal cancer (CRC) screening/surveillance in randomized controlled trials (RCTs) but has not shown benefit in real-world implementation studies. We performed a single-center pragmatic RCT to evaluate the impact of real-time CADe on ADRs in colonoscopy performed by community gastroenterologists. METHODS We enrolled 1100 patients undergoing colonoscopy for CRC screening, surveillance, positive fecal-immunohistochemical tests, and diagnostic indications at one community-based center from September 2022 to March 2023. Patients were randomly assigned (1:1) to traditional colonoscopy or real-time CADe. Blinded pathologists analyzed histopathologic findings. The primary outcome was ADR (the percentage of patients with at least 1 histologically proven adenoma or carcinoma). Secondary outcomes were adenomas detected per colonoscopy (APC), sessile-serrated lesion detection rate, and non-neoplastic resection rate. RESULTS The median age was 55.5 years (interquartile range, 50-62 years), 61% were female, 72.7% were of Hispanic ethnicity, and 9.1% had inadequate bowel preparation. The ADR for the CADe group was significantly higher than the traditional colonoscopy group (42.5% vs 34.4%; P = .005). The mean APC was significantly higher in the CADe group compared with the traditional colonoscopy group (0.89 ± 1.46 vs 0.60 ± 1.12; P < .001). The improvement in adenoma detection was driven by increased detection of <5 mm adenomas. CADe had a higher sessile-serrated lesion detection rate than traditional colonoscopy (4.7% vs 2.0%; P = .01). The improvement in ADR with CADe was significantly higher in the first half of the study (47.2% vs 33.7%; P = .002) compared with the second half (38.7% vs 34.9%; P = .33). CONCLUSIONS In a single-center pragmatic RCT, real-time CADe modestly improved ADR and APC in average-detector community endoscopists. (ClinicalTrials.gov number, NCT05963724).
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Affiliation(s)
- Nikhil R Thiruvengadam
- Division of Gastroenterology and Hepatology, Riverside University Health System, Moreno Valley, California; Division of Gastroenterology and Hepatology, Loma Linda University Health, Loma Linda, California.
| | - Pejman Solaimani
- Division of Gastroenterology and Hepatology, Riverside University Health System, Moreno Valley, California; Division of Gastroenterology and Hepatology, Loma Linda University Health, Loma Linda, California
| | - Manish Shrestha
- Division of Gastroenterology and Hepatology, Riverside University Health System, Moreno Valley, California; Division of Gastroenterology and Hepatology, Loma Linda University Health, Loma Linda, California
| | - Seth Buller
- Loma Linda University School of Medicine, Loma Linda, California
| | - Rachel Carson
- Division of Gastroenterology and Hepatology, Riverside University Health System, Moreno Valley, California; Division of Gastroenterology and Hepatology, Loma Linda University Health, Loma Linda, California
| | - Breanna Reyes-Garcia
- Division of Gastroenterology and Hepatology, Riverside University Health System, Moreno Valley, California; Division of Gastroenterology and Hepatology, Loma Linda University Health, Loma Linda, California
| | - Ronaldo D Gnass
- Department of Pathology, Riverside University Health System, Moreno Valley, California
| | - Bing Wang
- Department of Pathology, Loma Linda University School of Medicine, Loma Linda, California
| | - Natalie Albasha
- University of California Riverside School of Medicine, Riverside, California; Department of Medicine, Scripps Green Hospital, La Jolla, California
| | - Paul Leonor
- Division of Gastroenterology and Hepatology, Riverside University Health System, Moreno Valley, California; Division of Gastroenterology and Hepatology, Loma Linda University Health, Loma Linda, California
| | - Monica Saumoy
- Center for Digestive Health, Penn Medicine Princeton Medical Center, Plainsboro, New Jersey
| | - Raul Coimbra
- Comparative Effectiveness and Clinical Outcomes Research Center, Riverside University Health System, Moreno Valley, California; Department of Surgery, Riverside University Health System, Moreno Valley, California
| | - Arnold Tabuenca
- Department of Surgery, Riverside University Health System, Moreno Valley, California; Department of Surgery, University of California Riverside School of Medicine, Riverside, California
| | - Wichit Srikureja
- Division of Gastroenterology and Hepatology, Riverside University Health System, Moreno Valley, California; Division of Gastroenterology and Hepatology, Loma Linda University Health, Loma Linda, California
| | - Steve Serrao
- Division of Gastroenterology and Hepatology, Riverside University Health System, Moreno Valley, California; Division of Gastroenterology and Hepatology, Loma Linda University Health, Loma Linda, California
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Cherubini A, Dinh NN. A Review of the Technology, Training, and Assessment Methods for the First Real-Time AI-Enhanced Medical Device for Endoscopy. Bioengineering (Basel) 2023; 10:bioengineering10040404. [PMID: 37106592 PMCID: PMC10136070 DOI: 10.3390/bioengineering10040404] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 02/25/2023] [Accepted: 03/22/2023] [Indexed: 04/29/2023] Open
Abstract
Artificial intelligence (AI) has the potential to assist in endoscopy and improve decision making, particularly in situations where humans may make inconsistent judgments. The performance assessment of the medical devices operating in this context is a complex combination of bench tests, randomized controlled trials, and studies on the interaction between physicians and AI. We review the scientific evidence published about GI Genius, the first AI-powered medical device for colonoscopy to enter the market, and the device that is most widely tested by the scientific community. We provide an overview of its technical architecture, AI training and testing strategies, and regulatory path. In addition, we discuss the strengths and limitations of the current platform and its potential impact on clinical practice. The details of the algorithm architecture and the data that were used to train the AI device have been disclosed to the scientific community in the pursuit of a transparent AI. Overall, the first AI-enabled medical device for real-time video analysis represents a significant advancement in the use of AI for endoscopies and has the potential to improve the accuracy and efficiency of colonoscopy procedures.
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Affiliation(s)
- Andrea Cherubini
- Cosmo Intelligent Medical Devices, D02KV60 Dublin, Ireland
- Milan Center for Neuroscience, University of Milano-Bicocca, 20126 Milano, Italy
| | - Nhan Ngo Dinh
- Cosmo Intelligent Medical Devices, D02KV60 Dublin, Ireland
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The Role of Artificial Intelligence in Monitoring Inflammatory Bowel Disease-The Future Is Now. Diagnostics (Basel) 2023; 13:diagnostics13040735. [PMID: 36832222 PMCID: PMC9954871 DOI: 10.3390/diagnostics13040735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 02/02/2023] [Accepted: 02/02/2023] [Indexed: 02/17/2023] Open
Abstract
Crohn's disease and ulcerative colitis remain debilitating disorders, characterized by progressive bowel damage and possible lethal complications. The growing number of applications for artificial intelligence in gastrointestinal endoscopy has already shown great potential, especially in the field of neoplastic and pre-neoplastic lesion detection and characterization, and is currently under evaluation in the field of inflammatory bowel disease management. The application of artificial intelligence in inflammatory bowel diseases can range from genomic dataset analysis and risk prediction model construction to the disease grading severity and assessment of the response to treatment using machine learning. We aimed to assess the current and future role of artificial intelligence in assessing the key outcomes in inflammatory bowel disease patients: endoscopic activity, mucosal healing, response to treatment, and neoplasia surveillance.
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Rex DK. Top tips for maximum detection during colonoscope withdrawal (with video). Gastrointest Endosc 2022; 96:1050-1054. [PMID: 35948181 DOI: 10.1016/j.gie.2022.07.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 07/15/2022] [Accepted: 07/30/2022] [Indexed: 02/08/2023]
Affiliation(s)
- Douglas K Rex
- Division of Gastroenterology/Hepatology, Indiana University School of Medicine, Indianapolis, Indiana, USA
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