In EBioMedicine
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.
Berbís M Alvaro, McClintock David S, Bychkov Andrey, Van der Laak Jeroen, Pantanowitz Liron, Lennerz Jochen K, Cheng Jerome Y, Delahunt Brett, Egevad Lars, Eloy Catarina, Farris Alton B, Fraggetta Filippo, García Del Moral Raimundo, Hartman Douglas J, Herrmann Markus D, Hollemans Eva, Iczkowski Kenneth A, Karsan Aly, Kriegsmann Mark, Salama Mohamed E, Sinard John H, Tuthill J Mark, Williams Bethany, Casado-Sánchez César, Sánchez-Turrión Víctor, Luna Antonio, Aneiros-Fernández José, Shen Jeanne
2023-Jan-03
Anatomic pathology, Artificial intelligence, Computational pathology, Digital pathology, Machine learning, Pathologist workflow
