Computational pathology in 2030: a Delphi study forecasting the role of AI in pathology within the next decade.

Autor:	Berbís MA; Department of R&D, HT Médica, San Juan de Dios Hospital, Córdoba, Spain; Faculty of Medicine, Autonomous University of Madrid, Madrid, Spain. Electronic address: a.berbis@htime.org., McClintock DS; Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA., Bychkov A; Department of Pathology, Kameda Medical Center, Kamogawa, Chiba, Japan., Van der Laak J; Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands., Pantanowitz L; Department of Pathology, University of Michigan, Ann Arbor, MI, USA., Lennerz JK; Department of Pathology, Center for Integrated Diagnostics, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA., Cheng JY; Department of Pathology, University of Michigan, Ann Arbor, MI, USA., Delahunt B; Wellington School of Medicine and Health Sciences, University of Otago, Wellington, New Zealand., Egevad L; Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden., Eloy C; Pathology Laboratory, Institute of Molecular Pathology and Immunology, University of Porto, Porto, Portugal., Farris AB 3rd; Department of Pathology and Laboratory Medicine, Emory University, Atlanta, GA, USA., Fraggetta F; Pathology Unit, Azienda Sanitaria Provinciale Catania, Gravina Hospital, Caltagirone, Italy., García Del Moral R; Department of Pathology, San Cecilio Clinical University Hospital, Granada, Spain., Hartman DJ; Department of Anatomic Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA., Herrmann MD; Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA., Hollemans E; Department of Pathology, Erasmus University Medical Center, Rotterdam, the Netherlands., Iczkowski KA; Department of Pathology, Medical College of Wisconsin, Milwaukee, WI, USA., Karsan A; Department of Pathology & Laboratory Medicine, University of British Columbia, Michael Smith Genome Sciences Centre, Vancouver, Canada., Kriegsmann M; Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany., Salama ME; Department of Pathology, Sonic Healthcare, Austin, TX, USA., Sinard JH; Department of Pathology, Yale University School of Medicine, New Haven, CT, USA., Tuthill JM; Department of Pathology, Henry Ford Hospital, Detroit, MI, USA., Williams B; Department of Histopathology, Leeds Teaching Hospitals NHS Trust, Leeds, UK., Casado-Sánchez C; Department of Plastic and Reconstructive Surgery, La Paz University Hospital, Madrid, Spain., Sánchez-Turrión V; Department of General Surgery and Digestive Tract, Puerta de Hierro-Majadahonda University Hospital, Madrid, Spain., Luna A; Department of Integrated Diagnostics, HT Médica, Clínica Las Nieves, Jaén, Spain., Aneiros-Fernández J; Department of R&D, HT Médica, San Juan de Dios Hospital, Córdoba, Spain; Pathology Unit, Azienda Sanitaria Provinciale Catania, Gravina Hospital, Caltagirone, Italy., Shen J; Department of Pathology and Center for Artificial Intelligence in Medicine & Imaging, Stanford University School of Medicine, Stanford, CA, USA. Electronic address: jeannes@stanford.edu.
Jazyk:	angličtina
Zdroj:	EBioMedicine [EBioMedicine] 2023 Feb; Vol. 88, pp. 104427. Date of Electronic Publication: 2023 Jan 04.
DOI:	10.1016/j.ebiom.2022.104427
Abstrakt:	Background: Artificial intelligence (AI) is rapidly fuelling a fundamental transformation in the practice of pathology. However, clinical integration remains challenging, with no AI algorithms to date in routine adoption within typical anatomic pathology (AP) laboratories. This survey gathered current expert perspectives and expectations regarding the role of AI in AP from those with first-hand computational pathology and AI experience. Methods: Perspectives were solicited using the Delphi method from 24 subject matter experts between December 2020 and February 2021 regarding the anticipated role of AI in pathology by the year 2030. The study consisted of three consecutive rounds: 1) an open-ended, free response questionnaire generating a list of survey items; 2) a Likert-scale survey scored by experts and analysed for consensus; and 3) a repeat survey of items not reaching consensus to obtain further expert consensus. Findings: Consensus opinions were reached on 141 of 180 survey items (78.3%). Experts agreed that AI would be routinely and impactfully used within AP laboratory and pathologist clinical workflows by 2030. High consensus was reached on 100 items across nine categories encompassing the impact of AI on (1) pathology key performance indicators (KPIs) and (2) the pathology workforce and specific tasks performed by (3) pathologists and (4) AP lab technicians, as well as (5) specific AI applications and their likelihood of routine use by 2030, (6) AI's role in integrated diagnostics, (7) pathology tasks likely to be fully automated using AI, and (8) regulatory/legal and (9) ethical aspects of AI integration in pathology. Interpretation: This systematic consensus study details the expected short-to-mid-term impact of AI on pathology practice. These findings provide timely and relevant information regarding future care delivery in pathology and raise key practical, ethical, and legal challenges that must be addressed prior to AI's successful clinical implementation. Funding: No specific funding was provided for this study. Competing Interests: Declaration of interests M.A.B. is a board member of Cells IA Technologies; D.S.M. received consulting fees from, and is a scientific advisory board member of, Epredia and Roche, received honoraria for a sponsored presentation from Roche, and holds a leadership or fiduciary role in the Digital Pathology Association (DPA); J.V.L. received research funding from ContextVision, Sectra, and Philips, consulting fees from, and is a scientific advisory board member of, ContextVision and Philips, is a member of the Board of Directors of the DPA, Chair of the AI Taskforce of the European Society of Pathology, and is Chief Scientific Officer of, and holds stocks or stock options from, Aiosyn B.V.; L.P. received consulting fees from Hamamatsu and Ibex, has patents planned, issued or pending (LeanAP Innovators), holds an unpaid leadership or fiduciary role in other board, society, committee or advocacy group (DPA and ASC), and is a shareholder of Ibex; C.E. received consulting fees from Mindpeak, payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Leica and 3DHISTECH, and payment for expert testimony from MSD; D.J.H received royalties from Up-To-Date/LWW for the creation of educational content, consulting fees from IQVIA/Genae and VitaDx, and is a board member and shareholder of Techcyte Inc.; M.D.H received research funding from the National Cancer Institute (NCI), National Institutes of Health (NIH), and support for attending meetings and/or travel from the College of American Pathologists (CAP), DPA, and European Society for Digital and Integrative Pathology, and holds an unpaid leadership or fiduciary role in the DPA; M.E.S. and is a board member and shareholder of Techcyte Inc.; B.W. received honoraria for presentations from Leica Biosystems and is a scientific advisory board member of Paige AI; A.L. received honoraria from General Electric for lectures, and is a board member of Siemens Healthineers and Cells IA Technologies; J.A.F. is a shareholder of Cells IA Technologies; J.S. received institutional research funding from Google/Alphabet Inc. and Lunit Inc., consulting fees from KCK MedTech, and is an advisory board member of Crosscope, Inc. The remaining authors declare no competing interests. (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu