Computational pathology in 2030: a Delphi study forecasting the role of AI in pathology within the next decadeResearch in context
M. Alvaro Berbís,
David S. McClintock,
Andrey Bychkov,
Jeroen Van der Laak,
Liron Pantanowitz,
Jochen K. Lennerz,
Jerome Y. Cheng,
Brett Delahunt,
Lars Egevad,
Catarina Eloy,
Alton B. Farris, III,
Filippo Fraggetta,
Raimundo García del Moral,
Douglas J. Hartman,
Markus D. Herrmann,
Eva Hollemans,
Kenneth A. Iczkowski,
Aly Karsan,
Mark Kriegsmann,
Mohamed E. Salama,
John H. Sinard,
J. Mark Tuthill,
Bethany Williams,
César Casado-Sánchez,
Víctor Sánchez-Turrión,
Antonio Luna,
José Aneiros-Fernández,
Jeanne Shen
Affiliations
M. Alvaro Berbís
Department of R&D, HT Médica, San Juan de Dios Hospital, Córdoba, Spain; Faculty of Medicine, Autonomous University of Madrid, Madrid, Spain; Corresponding author. Department of R&D, HT Médica, San Juan de Dios Hospital, Córdoba, 14011, Spain.
David S. McClintock
Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
Andrey Bychkov
Department of Pathology, Kameda Medical Center, Kamogawa, Chiba, Japan
Jeroen Van der Laak
Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands
Liron Pantanowitz
Department of Pathology, University of Michigan, Ann Arbor, MI, USA
Jochen K. Lennerz
Department of Pathology, Center for Integrated Diagnostics, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA
Jerome Y. Cheng
Department of Pathology, University of Michigan, Ann Arbor, MI, USA
Brett Delahunt
Wellington School of Medicine and Health Sciences, University of Otago, Wellington, New Zealand
Lars Egevad
Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden
Catarina Eloy
Pathology Laboratory, Institute of Molecular Pathology and Immunology, University of Porto, Porto, Portugal
Alton B. Farris, III
Department of Pathology and Laboratory Medicine, Emory University, Atlanta, GA, USA
Filippo Fraggetta
Pathology Unit, Azienda Sanitaria Provinciale Catania, Gravina Hospital, Caltagirone, Italy
Raimundo García del Moral
Department of Pathology, San Cecilio Clinical University Hospital, Granada, Spain
Douglas J. Hartman
Department of Anatomic Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
Markus D. Herrmann
Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
Eva Hollemans
Department of Pathology, Erasmus University Medical Center, Rotterdam, the Netherlands
Kenneth A. Iczkowski
Department of Pathology, Medical College of Wisconsin, Milwaukee, WI, USA
Aly Karsan
Department of Pathology & Laboratory Medicine, University of British Columbia, Michael Smith Genome Sciences Centre, Vancouver, Canada
Mark Kriegsmann
Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
Mohamed E. Salama
Department of Pathology, Sonic Healthcare, Austin, TX, USA
John H. Sinard
Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
J. Mark Tuthill
Department of Pathology, Henry Ford Hospital, Detroit, MI, USA
Bethany Williams
Department of Histopathology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
César Casado-Sánchez
Department of Plastic and Reconstructive Surgery, La Paz University Hospital, Madrid, Spain
Víctor Sánchez-Turrión
Department of General Surgery and Digestive Tract, Puerta de Hierro-Majadahonda University Hospital, Madrid, Spain
Antonio Luna
Department of Integrated Diagnostics, HT Médica, Clínica Las Nieves, Jaén, Spain
José Aneiros-Fernández
Department of R&D, HT Médica, San Juan de Dios Hospital, Córdoba, Spain; Pathology Unit, Azienda Sanitaria Provinciale Catania, Gravina Hospital, Caltagirone, Italy
Jeanne Shen
Department of Pathology and Center for Artificial Intelligence in Medicine & Imaging, Stanford University School of Medicine, Stanford, CA, USA; Corresponding author. Department of Pathology and Center for Artificial Intelligence in Medicine & Imaging, Stanford University School of Medicine, Stanford, CA 94305, USA.
Summary: 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.
