Dermatologists’ Perspectives on Artificial Intelligence and Augmented Intelligence — A Cross-sectional Survey | Dermatology | JAMA Dermatology | JAMA Network
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Table 1.  Characteristics of Dermatologist-Respondents
Characteristics of Dermatologist-Respondents
Table 2.  Dermatologists’ Perspectives on Artificial Intelligence (AI) and Augmented Intelligence (AuI)
Dermatologists’ Perspectives on Artificial Intelligence (AI) and Augmented Intelligence (AuI)
1.
Kovarik  C, Lee  I, Ko  J; Ad Hoc Task Force on Augmented Intelligence.  Commentary: position statement on augmented intelligence (AuI).   J Am Acad Dermatol. 2019;81(4):998-1000. doi:10.1016/j.jaad.2019.06.032 PubMedGoogle ScholarCrossref
2.
Hekler  A, Utikal  JS, Enk  AH,  et al.  Superior skin cancer classification by the combination of human and artificial intelligence.   Eur J Cancer. 2019;120:114-121. doi:10.1016/j.ejca.2019.07.019PubMedGoogle ScholarCrossref
3.
Han  SS, Park  I, Eun Chang  S,  et al.  Augmented intelligence dermatology: deep neural networks empower medical professionals in diagnosing skin cancer and predicting treatment options for 134 skin disorders.   J Invest Dermatol. 2020;140(9):1753-1761. doi:10.1016/j.jid.2020.01.019 PubMedGoogle ScholarCrossref
4.
Zakhem  GA, Fakhoury  JW, Motosko  CC, Ho  RS.  Characterizing the role of dermatologists in developing artificial intelligence for assessment of skin cancer: A systematic review.   J Am Acad Dermatol. 2020;S0190-9622(20)30079-7. doi:10.1016/j.jaad.2020.01.028 PubMedGoogle Scholar
5.
Nelson  CA, Pérez-Chada  LM, Creadore  A,  et al.  Patient perspectives on the use of artificial intelligence for skin cancer screening: a qualitative study.   JAMA Dermatol. 2020;156(5):501-512. doi:10.1001/jamadermatol.2019.5014 PubMedGoogle ScholarCrossref
6.
Shen  C, Li  C, Xu  F,  et al.  Web-based study on Chinese dermatologists’ attitudes towards artificial intelligence.   Ann Transl Med. 2020;8(11):698. doi:10.21037/atm.2019.12.102 PubMedGoogle ScholarCrossref
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    Research Letter
    May 26, 2021

    Dermatologists’ Perspectives on Artificial Intelligence and Augmented Intelligence — A Cross-sectional Survey

    Author Affiliations
    • 1Department of Dermatology, Yale School of Medicine, New Haven, Connecticut
    • 2American Academy of Dermatology, Rosemont, Illinois
    • 3Department of Dermatology, Stanford University School of Medicine, Palo Alto, California
    • 4Department of Dermatology, Perelman School of Medicine, University of Pennsylvania, Philadelphia
    • 5Department of Internal Medicine, Division of Infectious Diseases, Perelman School of Medicine, University of Pennsylvania, Philadelphia
    JAMA Dermatol. 2021;157(7):871-874. doi:10.1001/jamadermatol.2021.1685

    Artificial intelligence (AI) refers to the ability of a machine or computer program to solve problems that would be typically handled by humans. Augmented intelligence (AuI) focuses on the assistive role of AI and is designed to enhance, but not replace, human intelligence and the physician-patient relationship.1 Studies have demonstrated2,3 superior skin cancer classification using a combination of dermatologists and AI and improved prognostication of malignant neoplasms by dermatologists using AI. A recent systematic review4 called for dermatologists’ leadership in defining how these technologies fit into clinical practice.

    Methods

    To evaluate dermatologists’ perspectives on AI and AuI, the American Academy of Dermatology (AAD) Task Force on Augmented Intelligence conducted a cross-sectional survey of 3080 fellows from July 10 to July 30, 2020. Continuous variables were summarized with means and standard deviations. Categorical variables were reported as proportions and percentages. Descriptive statistical analyses were performed from August 3 to September 23, 2020, using Excel 14.7.1 (Microsoft Corp).

    The AAD reviewed and approved the study, waiving informed consent because the study used only deidentified data. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline was followed.

    Results

    Characteristics of the 121 dermatologists (mean [SD] age, 51 [12] years; 64 [53%] men) who completed the survey (response rate, 3.9%) are detailed in Table 1. Most respondents self-reported their race/ethnicity as White (76; 84%) and non-Hispanic/Latino (93; 95%). Respondents represented all career stages and diverse physician practice settings.

    Dermatologists’ perspectives on AI and AuI are summarized in Table 2. While only 56 (46%) thought AI would positively influence their practice, that opinion rose to 78 (64%) for AuI. Sixty-nine (57%) stated they would use an AI tool with similar accuracy to a human dermatologist to help diagnose skin lesions in clinic. Ninety-one (76%) respondents stated that they would be more likely to perform a biopsy of a lesion that was not clinically suggestive of cancer if an AI tool indicated a possible malignant diagnosis. Also, 9 (8%) respondents stated that if an AI tool indicated a benign diagnosis, they would be less likely to perform a biopsy on a clinically suggestive lesion. Eighty-nine (74%) respondents said they would use an AI tool with similar accuracy to a human dermatologist to help monitor skin lesions.

    The survey instrument asked dermatologists to prioritize 3 benefits, risks, strengths, and weaknesses of AI tools for skin cancer screening identified by patients in a qualitative study.5 As benefits, dermatologists collectively prioritized more efficient triage, improved health care access, and quicker diagnosis. As risks, dermatologists collectively prioritized lack of patient follow-up owing to using AI without clinician support, clinician loss of control to AI, and human deskilling. Thirty-three (28%) dermatologists thought AI would exacerbate health care disparities. As strengths, dermatologists collectively prioritized patient motivation to seek skin cancer diagnosis or treatment, more objective diagnosis, and more convenient diagnosis. As weaknesses, dermatologists collectively prioritized the inability to perform total body skin examinations, lack of creativity owing to algorithmic limitations, and a lack of social contact between AI and the patient. Finally, most of the dermatologist-respondents identified disruption of the human physician-patient relationship and threats to accuracy as implementation challenges.

    Discussion

    This cross-sectional study indicates that dermatologists are receptive to assistance from AI tools in diagnosing and monitoring skin lesions but value the human physician-patient relationship and accuracy. These findings confirm and complement those of a recently published survey of Chinese dermatologists,6 which found that 99.5% are attentive to information on AI and 95.4% envision a role for AuI in dermatology. Dermatologists were more likely to biopsy a nonsuggestive lesion if the tool indicated a malignant diagnosis than to forego a biopsy of a suggestive lesion if the tool indicated a benign diagnosis. Future research would be beneficial to determine whether AuI increases biopsy rates in clinical practice.

    The very low survey response rate of this study strongly limits the conclusions and generalizability of its findings. It is unknown whether respondents were users of AI or AuI, which may have introduced bias into the spectrum of responses. Although respondents’ demographic information was aligned with that of the general population of AAD fellows (eg, mean age, 50 years; 47% men), further research is needed to confirm the key finding, that is, that most dermatologists are open to integrating AI and AuI with clinical practice.

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    Article Information

    Accepted for Publication: April 7, 2021.

    Published Online: May 26, 2021. doi:10.1001/jamadermatol.2021.1685

    Corresponding Author: Carrie L. Kovarik, MD, Department of Dermatology, Perelman School of Medicine, University of Pennsylvania, Two Maloney Building, 3600 Spruce St, Philadelphia, PA 19104 (carrie.kovarik@pennmedicine.upenn.edu).

    Author Contributions: Drs Kovarik and Nelson had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: All authors.

    Acquisition, analysis, or interpretation of data: Nelson, Balk, Miller, Theunis, Kovarik.

    Drafting of the manuscript: Nelson, Theunis, Kovarik.

    Critical revision of the manuscript for important intellectual content: Pachauri, Balk, Miller, Ko, Kovarik.

    Statistical analysis: Nelson, Balk, Theunis.

    Administrative, technical, or material support: Pachauri, Balk, Miller, Kovarik.

    Supervision: Pachauri, Balk, Kovarik.

    Conflict of Interest Disclosures: Dr Ko reported serving as Chairperson of the AAD Augmented Intelligence Task Force. No other disclosures were reported.

    Additional Contributions: The authors wish to thank the following members of the American Academy of Dermatology Task Force on Augmented Intelligence: Ivy Lee, MD, FAAD, Pasadena Premier Dermatology; Adewole Shomari Adamson, MD, MPP, FAAD, Division of Dermatology, Department of Internal Medicine, Dell Medical School, The University of Texas at Austin; Susan Jen Huang, MD, FAAD, Palo Alto Foundation Medical Group of Sutter Health; Dhaval Bhanusali, MD, FAAD, Hudson Dermatology & Laser Surgery and Mount Sinai Hospital; Daniel M. Siegel, MD, MS, FAAD, SUNY Downstate Health Sciences University; Joseph C. Kvedar, MD, FAAD, Harvard Medical School; Jules Lipoff, MD, FAAD, Perelman School of Medicine, University of Pennsylvania; Roberto Novoa, MD, FAAD, Departments of Pathology and Dermatology, Stanford University. We also wish to thank Dr Arash Mostaghimi, MD, MPA, MPH, Department of Dermatology, Brigham and Women’s Hospital, for providing subject matter expertise that informed the development of the survey.

    References
    1.
    Kovarik  C, Lee  I, Ko  J; Ad Hoc Task Force on Augmented Intelligence.  Commentary: position statement on augmented intelligence (AuI).   J Am Acad Dermatol. 2019;81(4):998-1000. doi:10.1016/j.jaad.2019.06.032 PubMedGoogle ScholarCrossref
    2.
    Hekler  A, Utikal  JS, Enk  AH,  et al.  Superior skin cancer classification by the combination of human and artificial intelligence.   Eur J Cancer. 2019;120:114-121. doi:10.1016/j.ejca.2019.07.019PubMedGoogle ScholarCrossref
    3.
    Han  SS, Park  I, Eun Chang  S,  et al.  Augmented intelligence dermatology: deep neural networks empower medical professionals in diagnosing skin cancer and predicting treatment options for 134 skin disorders.   J Invest Dermatol. 2020;140(9):1753-1761. doi:10.1016/j.jid.2020.01.019 PubMedGoogle ScholarCrossref
    4.
    Zakhem  GA, Fakhoury  JW, Motosko  CC, Ho  RS.  Characterizing the role of dermatologists in developing artificial intelligence for assessment of skin cancer: A systematic review.   J Am Acad Dermatol. 2020;S0190-9622(20)30079-7. doi:10.1016/j.jaad.2020.01.028 PubMedGoogle Scholar
    5.
    Nelson  CA, Pérez-Chada  LM, Creadore  A,  et al.  Patient perspectives on the use of artificial intelligence for skin cancer screening: a qualitative study.   JAMA Dermatol. 2020;156(5):501-512. doi:10.1001/jamadermatol.2019.5014 PubMedGoogle ScholarCrossref
    6.
    Shen  C, Li  C, Xu  F,  et al.  Web-based study on Chinese dermatologists’ attitudes towards artificial intelligence.   Ann Transl Med. 2020;8(11):698. doi:10.21037/atm.2019.12.102 PubMedGoogle ScholarCrossref
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