Letter: The Urgency of Neurosurgical Leadership in the Era of Artificial Intelligence

Tang, Oliver Y.; Ali, Rohaid; Connolly, Ian D.; Fridley, Jared; Sullivan, Patricia L. Zadnik; Cielo, Deus; Oyelese, Adetokunbo A.; Doberstein, Curtis E.; Telfeian, Albert E.; Gokaslan, Ziya L.; Shin, John H.; Asaad, Wael

doi:10.1227/neu.0000000000002576

Cited by 5 publications

(7 citation statements)

References 11 publications

(14 reference statements)

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“…18 A prominent way AI can contribute to such disparities is by incorporating bias from unrepresentative training data sets. [4][5][6][7][8] This issue has already received at- An increase in the percentage of female attending surgeons in a specialty was associated with a rise in the percentage of female trainees (+1.3% for every 1.0% increase in female attending surgeons, P = .003), while there was no significant association between the percentage of non-White trainees and non-White attending surgeons within the specialties (P = .46).…”

Section: Discussionmentioning

confidence: 99%

“…Adoption of new medical technologies carries the potential for exacerbating, rather than ameliorating, disparities in patient outcomes due to differences in access, adoption, or clinical application . A prominent way AI can contribute to such disparities is by incorporating bias from unrepresentative training data sets . This issue has already received attention in the medical field, particularly in the context of potentially limited generalizability of clinical trial studies with inequitable recruitment of specific patient populations.…”

Section: Discussionmentioning

confidence: 99%

“…Researchers and developers must consider the impact of their models on society and work toward creating AI systems with appropriate training data sets, feedback mechanisms, and guardrails that accurately reflect our diverse world. Moreover, the active involvement of surgeons and other clinicians in the refinement of these models, such as serving in advisory roles, may help combat inaccuracies in the perception of the discipline of surgery by the general public …”

Section: Discussionmentioning

confidence: 99%

“…Moreover, the active involvement of surgeons and other clinicians in the refinement of these models, such as serving in advisory roles, may help combat inaccuracies in the perception of the discipline of surgery by the general public. 7,22…”

Section: Limitationsmentioning

confidence: 99%

“…These AI systems are trained on publicly available data and are therefore susceptible to learning and replicating biases. [4][5][6][7][8] In other words, if the training data lack representation or are skewed toward particular demographic groups, the AI models could produce outputs that reflect these biases. These misrepresentations can have tangible outcomes: for example, Amazon ceased the use of an experimental AI recruiting tool after determining that the model consistently returned lower ratings for female applicants having been trained on data that reflected historical gender-based hiring disparities.…”

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confidence: 99%

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Demographic Representation in 3 Leading Artificial Intelligence Text-to-Image Generators

Ali,

Tang,

Connolly

et al. 2024

JAMA Surg

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ImportanceThe progression of artificial intelligence (AI) text-to-image generators raises concerns of perpetuating societal biases, including profession-based stereotypes.ObjectiveTo gauge the demographic accuracy of surgeon representation by 3 prominent AI text-to-image models compared to real-world attending surgeons and trainees.Design, Setting, and ParticipantsThe study used a cross-sectional design, assessing the latest release of 3 leading publicly available AI text-to-image generators. Seven independent reviewers categorized AI-produced images. A total of 2400 images were analyzed, generated across 8 surgical specialties within each model. An additional 1200 images were evaluated based on geographic prompts for 3 countries. The study was conducted in May 2023. The 3 AI text-to-image generators were chosen due to their popularity at the time of this study. The measure of demographic characteristics was provided by the Association of American Medical Colleges subspecialty report, which references the American Medical Association master file for physician demographic characteristics across 50 states. Given changing demographic characteristics in trainees compared to attending surgeons, the decision was made to look into both groups separately. Race (non-White, defined as any race other than non-Hispanic White, and White) and gender (female and male) were assessed to evaluate known societal biases.ExposuresImages were generated using a prompt template, “a photo of the face of a [blank]”, with the blank replaced by a surgical specialty. Geographic-based prompting was evaluated by specifying the most populous countries on 3 continents (the US, Nigeria, and China).Main Outcomes and MeasuresThe study compared representation of female and non-White surgeons in each model with real demographic data using χ2, Fisher exact, and proportion tests.ResultsThere was a significantly higher mean representation of female (35.8% vs 14.7%; P &lt; .001) and non-White (37.4% vs 22.8%; P &lt; .001) surgeons among trainees than attending surgeons. DALL-E 2 reflected attending surgeons’ true demographic data for female surgeons (15.9% vs 14.7%; P = .39) and non-White surgeons (22.6% vs 22.8%; P = .92) but underestimated trainees’ representation for both female (15.9% vs 35.8%; P &lt; .001) and non-White (22.6% vs 37.4%; P &lt; .001) surgeons. In contrast, Midjourney and Stable Diffusion had significantly lower representation of images of female (0% and 1.8%, respectively; P &lt; .001) and non-White (0.5% and 0.6%, respectively; P &lt; .001) surgeons than DALL-E 2 or true demographic data. Geographic-based prompting increased non-White surgeon representation but did not alter female representation for all models in prompts specifying Nigeria and China.Conclusion and RelevanceIn this study, 2 leading publicly available text-to-image generators amplified societal biases, depicting over 98% surgeons as White and male. While 1 of the models depicted comparable demographic characteristics to real attending surgeons, all 3 models underestimated trainee representation. The study suggests the need for guardrails and robust feedback systems to minimize AI text-to-image generators magnifying stereotypes in professions such as surgery.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Limitationsmentioning

confidence: 99%

mentioning

confidence: 99%

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Demographic Representation in 3 Leading Artificial Intelligence Text-to-Image Generators

Ali,

Tang,

Connolly

et al. 2024

JAMA Surg

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The Face of a Surgeon: An Analysis of Demographic Representation in Three Leading Artificial Intelligence Text-to-Image Generators

Ali

Tang

Connolly

et al. 2023

Preprint

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BackgroundThis study investigates the accuracy of three prominent artificial intelligence (AI) text-to-image generators—DALL-E 2, Midjourney, and Stable Diffusion—in representing the demographic realities in the surgical profession, addressing raised concerns about the perpetuation of societal biases, especially profession-based stereotypes.MethodsA cross-sectional analysis was conducted on 2,400 images generated across eight surgical specialties by each model. An additional 1,200 images were evaluated based on geographic prompts for three countries. Images were generated using a prompt template, “A photo of the face of a [blank]”, with blank replaced by a surgical specialty. Geographic-based prompting was evaluated by specifying the most populous countries for three continents (United States, Nigeria, and China).ResultsThere was a significantly higher representation of female (average=35.8% vs. 14.7%, P<0.001) and non-white (average=37.4% vs. 22.8%, P<0.001) surgeons among trainees than attendings. DALL-E 2 reflected attendings’ true demographics for female surgeons (15.9% vs. 14.7%, P=0.386) and non-white surgeons (22.6% vs. 22.8%, P=0.919) but underestimated trainees’ representation for both female (15.9% vs. 35.8%, P<0.001) and non-white (22.6% vs. 37.4%, P<0.001) surgeons. In contrast, Midjourney and Stable Diffusion had significantly lower representation of images of female (0% and 1.8%, respectively) and non-white (0.5% and 0.6%, respectively) surgeons than DALL-E 2 or true demographics (all P<0.001). Geographic-based prompting increased non-white surgeon representation (all P<0.001), but did not alter female representation (P=0.779).ConclusionsWhile Midjourney and Stable Diffusion amplified societal biases by depicting over 98% of surgeons as white males, DALL-E 2 depicted more accurate demographics, although all three models underestimated trainee representation. These findings underscore the necessity for guardrails and robust feedback systems to prevent AI text-to-image generators from exacerbating profession-based stereotypes, and the importance of bolstering the representation of the evolving surgical field in these models’ future training sets.

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Leadership Challenges and Strategies in the Era of AI Transformation

Singh

2023

2023 International Conference on Computational Science and Computational Intelligence (CSCI)

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Letter: The Urgency of Neurosurgical Leadership in the Era of Artificial Intelligence

Cited by 5 publications

References 11 publications

Demographic Representation in 3 Leading Artificial Intelligence Text-to-Image Generators

Demographic Representation in 3 Leading Artificial Intelligence Text-to-Image Generators

The Face of a Surgeon: An Analysis of Demographic Representation in Three Leading Artificial Intelligence Text-to-Image Generators

Leadership Challenges and Strategies in the Era of AI Transformation

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