The Ethics of Artificial Intelligence in Pathology and Laboratory Medicine: Principles and Practice

Jackson, Brian R.; Ye, Ye; Crawford, James M.; Becich, Michael J.; Botkin, Jeffrey R.; Baca, Monica E. de; Pantanowitz, Liron

doi:10.1177/2374289521990784

Cited by 40 publications

(20 citation statements)

References 72 publications

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“…Therefore, the current lack of consensus among the panelists regarding the preceding topics is understandable and expected to be resolved as (1) more AI tools reach maturity and come to be evaluated in prospective clinical settings, and (2) more consideration is directed toward ensuring that AI models are integrated into pathology workflows in a manner that maximizes accuracy, time and cost efficiency, safety, transparency, accountability, and positive impact on patient outcomes. 8,9,15,49,72…”

Section: Discussionmentioning

confidence: 99%

“…1,2 Excitement and expectations regarding the potential of AI to revolutionize healthcare have continued to build, as evidenced by the growing list of medical AI publications in the form of original research articles, review papers, health policy reports, white papers and consensus recommendations from professional societies, and coverage in the popular media. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] A recent survey of English-language articles indexed in PubMed showed a significant increase in the volume of medical AI research publications, from just 203 articles in 2005 to 12,563 in 2019. 3 Pathology has attracted growing attention as an image-rich specialty likely to be strongly impacted by recent advances in AI.…”

Section: Introductionmentioning

confidence: 99%

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The future of computational pathology: expectations regarding the anticipated role of artificial intelligence in pathology by 2030

Berbís¹,

McClintock

Bychkov

et al. 2022

Preprint

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Background: Artificial intelligence (AI) is rapidly fueling a fundamental transformation in the practice of pathology. However, the clinical integration of AI remains challenging, with no AI algorithms to date enjoying 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 analyzed 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) role of AI 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 is the first systematic consensus study detailing the expected short/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 the successful clinical implementation of AI.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The future of computational pathology: expectations regarding the anticipated role of artificial intelligence in pathology by 2030

Berbís¹,

McClintock

Bychkov

et al. 2022

Preprint

Self Cite

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“…Literature discussing the potential benefits of DP/AI rarely mention the ethical and legal considerations of access to, and processing of, patient data [45][46][47][48][49][50][51]. Advice/guidance on medical ethics and data governance exists, although the depth to which these topics are explored varies significantly (Table 1).…”

Section: Introductionmentioning

confidence: 99%

Understanding the ethical and legal considerations of Digital Pathology

Coulter

McKay

Hallowell

et al. 2021

The Journal of Pathology CR

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Digital Pathology (DP) is a platform which has the potential to develop a truly integrated and global pathology community. The generation of DP data at scale creates novel challenges for the histopathology community in managing, processing, and governing the use of these data. The current understanding of, and confidence in, the legal and ethical aspects of DP by pathologists is unknown. We developed an electronic survey (e-survey), comprising 22 questions, with input from the Royal College of Pathologists (RCPath) Digital Pathology Working Group. The e-survey was circulated via e-mail and social media (Twitter) through the RCPath Digital Pathology Working Group network, RCPath Trainee Committee network, the Pathology image data Lake for Analytics, Knowledge and Education (PathLAKE) digital pathology consortium, National Pathology Imaging Co-operative (NPIC), local contacts, and to the membership of both The Pathological Society of Great Britain and Ireland and the British Division of the International Academy of Pathology (BDIAP). Between 14 July 2020 and 6 September 2020, we collected 198 responses representing a cross section of histopathologists, including individuals with experience of DP research. We ascertained that, in the UK, DP is being used for diagnosis, research, and teaching, and that the platform is enabling data sharing. Our survey demonstrated that there is often a lack of confidence and understanding of the key issues of consent, legislation, and ethical guidelines. Of 198 respondents, 82 (41%) did not know when the use of digital scanned slide images would fall under the relevant legislation and 93 (47%) were 'Not confident at all' in their interpretation of consent for scanned slide images in research. With increasing uptake of DP, a working knowledge of these areas is essential but histopathologists often express a lack of confidence in these topics. The need for specific training in these areas is highlighted by the findings of this study.

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“…While writing this paper, we identified an article with a similar thematic focus examining the role of AI ethics in pathology and laboratory medicine. 25 The article by Jackson et al 25 discusses AI ethics from a traditional bioethicist's perspective, relating to the core principles of bioethics as laid down in the Belmont report (https://www.hhs.gov/ohrp/ regulations-and-policy/belmont-report/read-the-belmontreport/index.html, last accessed June 24, 2021). In contrast, the current mini-review mainly focuses on AI ethics from the perspective of ongoing developments in the area of AI research and algorithmic decision-making (ADM) and imagining its potential impact on the future practice of pathology.…”

mentioning

confidence: 99%

Ethics of AI in Pathology

Chauhan

Gullapalli

2021

The American Journal of Pathology

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Deep learning has rapidly advanced artificial intelligence (AI) and algorithmic decision-making (ADM) paradigms, affecting many traditional fields of medicine. Pathology is a heavily data-centric specialty of medicine. The structured nature of pathology data repositories makes it highly attractive to AI researchers to train deep learning models to improve health care delivery. Equally, there are enormous financial incentives driving adoption of AI and ADM due to promise of increased efficiency of the health care delivery process. Unethical use of AI may exacerbate existing inequities of health care, especially if not implemented correctly. There is an urgent need to harness the vast power of AI in an ethically and morally justifiable manner. In this mini-review, we explore the key issues involving AI ethics in pathology. Issues related to ethical design of pathology AI studies and the potential risks associated with implementation of AI and ADM within the pathology workflow are discussed. Three key foundational principles of ethical AI are described in the context of pathology: transparency, accountability, and governance. The future practice of pathology must be guided by these principles. Pathologists should be aware of the potential of AI to deliver superlative health care and the ethical pitfalls associated with it. Finally, pathologists must have a seat at the table to drive the future implementation of ethical AI in the practice of pathology.

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The Ethics of Artificial Intelligence in Pathology and Laboratory Medicine: Principles and Practice

Cited by 40 publications

References 72 publications

The future of computational pathology: expectations regarding the anticipated role of artificial intelligence in pathology by 2030

The future of computational pathology: expectations regarding the anticipated role of artificial intelligence in pathology by 2030

Understanding the ethical and legal considerations of Digital Pathology

Ethics of AI in Pathology

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