What Goes In, Must Come Out: Generative Artificial Intelligence Does Not Present Algorithmic Bias Across Race and Gender in Medical Residency Specialties.

Autor:	Lin S; Department of Medical Education, Nova Southeastern University Dr. Kiran C. Patel College of Allopathic Medicine, Fort Lauderdale, USA., Pandit S; Department of Medical Education, Nova Southeastern University Dr. Kiran C. Patel College of Allopathic Medicine, Fort Lauderdale, USA., Tritsch T; Department of Medical Education, Nova Southeastern University Dr. Kiran C. Patel College of Allopathic Medicine, Fort Lauderdale, USA., Levy A; Department of Medical Education, Nova Southeastern University Dr. Kiran C. Patel College of Allopathic Medicine, Fort Lauderdale, USA., Shoja MM; Department of Medical Education, Nova Southeastern University Dr. Kiran C. Patel College of Allopathic Medicine, Fort Lauderdale, USA.
Jazyk:	angličtina
Zdroj:	Cureus [Cureus] 2024 Feb 19; Vol. 16 (2), pp. e54448. Date of Electronic Publication: 2024 Feb 19 (Print Publication: 2024).
DOI:	10.7759/cureus.54448
Abstrakt:	Objective Artificial Intelligence (AI) has made significant inroads into various domains, including medicine, raising concerns about algorithmic bias. This study investigates the presence of biases in generative AI programs, with a specific focus on gender and racial representations across 19 medical residency specialties. Methodology This comparative study utilized DALL-E2 to generate faces representing 19 distinct residency training specialties, as identified by the Association of American Medical Colleges (AAMC), which were then compared to the AAMC's residency specialty breakdown with respect to race and gender. Results Our findings reveal an alignment between OpenAI's DALL-E2's predictions and the current demographic landscape of medical residents, suggesting an absence of algorithmic bias in this AI model. Conclusion This revelation gives rise to important ethical considerations. While AI excels at pattern recognition, it inherits and mirrors the biases present in its training data. To combat AI bias, addressing real-world disparities is imperative. Initiatives to promote inclusivity and diversity within medicine are commendable and contribute to reshaping medical education. This study underscores the need for ongoing efforts to dismantle barriers and foster inclusivity in historically male-dominated medical fields, particularly for underrepresented populations. Ultimately, our findings underscore the crucial role of real-world data quality in mitigating AI bias. As AI continues to shape healthcare and education, the pursuit of equitable, unbiased AI applications should remain at the forefront of these transformative endeavors. Competing Interests: The authors have declared that no competing interests exist. (Copyright © 2024, Lin et al.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu