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

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 3rd; 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

doi:10.1016/j.ebiom.2022.104427

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

EBioMedicine. 2023 Feb:88:104427. doi: 10.1016/j.ebiom.2022.104427. Epub 2023 Jan 4.

Authors

M Alvaro Berbís¹, David S McClintock², Andrey Bychkov³, Jeroen Van der Laak⁴, Liron Pantanowitz⁵, Jochen K Lennerz⁶, Jerome Y Cheng⁵, Brett Delahunt⁷, Lars Egevad⁸, Catarina Eloy⁹, Alton B Farris 3rd¹⁰, Filippo Fraggetta¹¹, Raimundo García Del Moral¹², Douglas J Hartman¹³, Markus D Herrmann¹⁴, Eva Hollemans¹⁵, Kenneth A Iczkowski¹⁶, Aly Karsan¹⁷, Mark Kriegsmann¹⁸, Mohamed E Salama¹⁹, John H Sinard²⁰, J Mark Tuthill²¹, Bethany Williams²², César Casado-Sánchez²³, Víctor Sánchez-Turrión²⁴, Antonio Luna²⁵, José Aneiros-Fernández²⁶, Jeanne Shen²⁷

Affiliations

¹ 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.
² Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA.
³ Department of Pathology, Kameda Medical Center, Kamogawa, Chiba, Japan.
⁴ Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands.
⁵ Department of Pathology, University of Michigan, Ann Arbor, MI, USA.
⁶ Department of Pathology, Center for Integrated Diagnostics, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA.
⁷ Wellington School of Medicine and Health Sciences, University of Otago, Wellington, New Zealand.
⁸ Department of Oncology-Pathology, Karolinska Institute, Stockholm, Sweden.
⁹ Pathology Laboratory, Institute of Molecular Pathology and Immunology, University of Porto, Porto, Portugal.
¹⁰ Department of Pathology and Laboratory Medicine, Emory University, Atlanta, GA, USA.
¹¹ Pathology Unit, Azienda Sanitaria Provinciale Catania, Gravina Hospital, Caltagirone, Italy.
¹² Department of Pathology, San Cecilio Clinical University Hospital, Granada, Spain.
¹³ Department of Anatomic Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
¹⁴ Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
¹⁵ Department of Pathology, Erasmus University Medical Center, Rotterdam, the Netherlands.
¹⁶ Department of Pathology, Medical College of Wisconsin, Milwaukee, WI, USA.
¹⁷ Department of Pathology & Laboratory Medicine, University of British Columbia, Michael Smith Genome Sciences Centre, Vancouver, Canada.
¹⁸ Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany.
¹⁹ Department of Pathology, Sonic Healthcare, Austin, TX, USA.
²⁰ Department of Pathology, Yale University School of Medicine, New Haven, CT, USA.
²¹ Department of Pathology, Henry Ford Hospital, Detroit, MI, USA.
²² Department of Histopathology, Leeds Teaching Hospitals NHS Trust, Leeds, UK.
²³ Department of Plastic and Reconstructive Surgery, La Paz University Hospital, Madrid, Spain.
²⁴ Department of General Surgery and Digestive Tract, Puerta de Hierro-Majadahonda University Hospital, Madrid, Spain.
²⁵ Department of Integrated Diagnostics, HT Médica, Clínica Las Nieves, Jaén, Spain.
²⁶ Department of R&D, HT Médica, San Juan de Dios Hospital, Córdoba, Spain; Pathology Unit, Azienda Sanitaria Provinciale Catania, Gravina Hospital, Caltagirone, Italy.
²⁷ Department of Pathology and Center for Artificial Intelligence in Medicine & Imaging, Stanford University School of Medicine, Stanford, CA, USA. Electronic address: jeannes@stanford.edu.

Abstract

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.

Keywords: Anatomic pathology; Artificial intelligence; Computational pathology; Digital pathology; Machine learning; Pathologist workflow.

MeSH terms

Algorithms*
Artificial Intelligence*
Delphi Technique
Forecasting
Humans
Surveys and Questionnaires