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

被引：58

作者：

Berbis, M. Alvaro ^{[1
,2
]}

McClintock, David S. ^{[3
]}

Bychkov, Andrey ^{[4
]}

Van der Laak, Jeroen ^{[5
]}

Pantanowitz, Liron ^{[6
]}

Lennerz, Jochen K. ^{[7
]}

Cheng, Jerome Y. ^{[6
]}

Delahunt, Brett ^{[8
]}

Egevad, Lars ^{[9
]}

Eloy, Catarina ^{[10
]}

Farris III, Alton B. ^{[11
]}

Fraggetta, Filippo ^{[12
]}

del Moral, Raimundo Garcia ^{[13
]}

Hartman, Douglas J. ^{[14
]}

Herrmann, Markus D. ^{[15
,16
]}

Hollemans, Eva ^{[17
]}

Iczkowski, Kenneth A. ^{[18
]}

Karsan, Aly ^{[19
]}

Kriegsmann, Mark ^{[20
]}

Salama, Mohamed E. ^{[21
]}

Sinard, John H. ^{[22
]}

Tuthill, J. Mark ^{[23
]}

Williams, Bethany ^{[24
]}

Casado-Sanchez, Cesar ^{[25
]}

Sanchez-Turrion, Victor ^{[26
]}

Luna, Antonio ^{[27
]}

Aneiros-Fernandez, Jose ^{[1
,12
]}

Shen, Jeanne ^{[28
,29
]}

机构：

[1] San Juan de Dios Hosp, Dept R&D, HT Med, Cordoba, Spain

[2] Autonomous Univ Madrid, Fac Med, Madrid, Spain

[3] Mayo Clin, Dept Lab Med & Pathol, Rochester, MN USA

[4] Kameda Med Ctr, Dept Pathol, Kamogawa, Chiba, Japan

[5] Radboud Univ Nijmegen Med Ctr, Dept Pathol, Nijmegen, Netherlands

[6] Univ Michigan, Dept Pathol, Ann Arbor, MI USA

[7] Harvard Med Sch, Massachusetts Gen Hosp, Ctr Integrated Diagnost, Dept Pathol, Boston, MA USA

[8] Univ Otago, Wellington Sch Med & Hlth Sci, Wellington, New Zealand

[9] Karolinska Inst, Dept Oncol Pathol, Stockholm, Sweden

[10] Univ Porto, Inst Mol Pathol & Immunol, Pathol Lab, Porto, CA, Portugal

[11] Emory Univ, Dept Pathol & Lab Med, Atlanta, GA USA

[12] Gravina Hosp, Azienda Sanitaria Prov Catania, Pathol Unit, Caltagirone, Italy

[13] San Cecilio Clin Univ Hosp, Dept Pathol, Granada, CA, Spain

[14] Univ Pittsburgh, Dept Anat Pathol, Med Ctr, Pittsburgh, PA USA

[15] Massachusetts Gen Hosp, Dept Pathol, Boston, MA USA

[16] Harvard Med Sch, Boston, MA USA

[17] Erasmus MC, Dept Pathol, Rotterdam, Netherlands

[18] Med Coll Wisconsin, Dept Pathol, Milwaukee, WI USA

[19] Univ British Columbia, Michael Smith Genome Sci Ctr, Dept Pathol & Lab Med, Vancouver, BC, Canada

[20] Univ Hosp Heidelberg, Inst Pathol, Heidelberg, Germany

[21] Sonic Healthcare, Dept Pathol, Austin, TX USA

[22] Yale Univ, Dept Pathol, Sch Med, New Haven, CT USA

[23] Henry Ford Hosp, Dept Pathol, Detroit, MI USA

[24] Leeds Teaching Hosp NHS Trust, Dept Histopathol, Leeds, England

[25] La Paz Univ Hosp, Dept Plast & Reconstruct Surg, Madrid, Spain

[26] Puerta de Hierro Majadahonda Univ Hosp, Dept Gen Surg & Digest Tract, Madrid, Spain

[27] Clin Las Nieves, Dept Integrated Diagnost, HT Med, Jaen, Spain

[28] Stanford Univ, Sch Med, Dept Pathol, Stanford, CA USA

[29] Stanford Univ, Sch Med, Ctr Artificial Intelligence Med & Imaging, Stanford, CA USA

来源：

EBIOMEDICINE | 2023年 / 88卷

关键词：

Arti fi cial intelligence; Machine learning; Digital pathology; Computational pathology; Anatomic pathology; ARTIFICIAL-INTELLIGENCE;

D O I：

10.1016/j.ebiom.2022.104427

中图分类号：

R5 [内科学];

学科分类号：

1002 ; 100201 ;

摘要：

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 inte-grated 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. Copyright (c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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