Name
Exploring Medical Student Perceptions of Artificial Intelligence in Medicine: Implications for Specialty Choice, Curriculum Design, and Rural Practice
Date & Time
Sunday, June 7, 2026, 4:00 PM - 4:15 PM
Location Name
Oglethorpe F
Speakers
Authors
Surya Donty, UT Tyler School of Medicine
Mohammed Aamir Sheikh, DO, UT Tyler Health Science Center
Abdelrazig Suliman, MD, UT Tyler Health Science Center
Chloe Duvak, UT Tyler School of Medicine
Presentation Topic(s)
Technology and Innovation
Description
PURPOSE
Artificial intelligence (AI) is transforming medicine by improving
diagnostic precision and clinical efficiency. Although AI offers significant
promise and is already embedded in tools used by clinicians, it has also
introduced uncertainty for medical students on specialty choice and future
job security. Prior international studies suggest AI may influence specialty
choice decisions, but U.S.-based data, especially from rural-focused
institutions, remain limited. This study aimed to characterize medical
student perceptions to inform curriculum development and workforce planning.
METHODS
A cross-sectional survey was administered to all three cohorts (n=120) at
the University of Texas at Tyler School of Medicine, a newer institution with
a mission to train primary care and rural physicians. Students completed a
Qualtrics-based questionnaire that included 5-point Likert items assessing
three domains: (1) perceived importance and benefits of AI in medicine, (2)
AI’s influence on specialty choice, and (3) interest in formal AI education.
Students were asked whether AI benefits patients and clinicians, whether
concerns about AI had reduced interest in specific specialties, and whether
an AI-focused course would be valuable in medical school.
RESULTS
Of 120 students, 48 responded (40% response rate). Most respondents (92%)
believed AI would benefit patients diagnostically, and 94% felt AI would
support physicians in decision-making. Regarding specialty choice, 41.6%
reported decreased interest in certain fields due to AI, while 54.2% reported
no change. Interest in AI education was high: 64.5% expressed interest in
formal training, 21% were neutral, and 14.5% were not interested.
CONCLUSION
In this rural-oriented medical school sample, students broadly endorsed
AI’s diagnostic and clinical value. Although AI influenced specialty interest
for a subset of students, more than half reported no change in their career
preferences. Strong student support for integrating AI content into the
curriculum highlights the need to prepare future physicians for an AI-enabled
healthcare environment.
Artificial intelligence (AI) is transforming medicine by improving
diagnostic precision and clinical efficiency. Although AI offers significant
promise and is already embedded in tools used by clinicians, it has also
introduced uncertainty for medical students on specialty choice and future
job security. Prior international studies suggest AI may influence specialty
choice decisions, but U.S.-based data, especially from rural-focused
institutions, remain limited. This study aimed to characterize medical
student perceptions to inform curriculum development and workforce planning.
METHODS
A cross-sectional survey was administered to all three cohorts (n=120) at
the University of Texas at Tyler School of Medicine, a newer institution with
a mission to train primary care and rural physicians. Students completed a
Qualtrics-based questionnaire that included 5-point Likert items assessing
three domains: (1) perceived importance and benefits of AI in medicine, (2)
AI’s influence on specialty choice, and (3) interest in formal AI education.
Students were asked whether AI benefits patients and clinicians, whether
concerns about AI had reduced interest in specific specialties, and whether
an AI-focused course would be valuable in medical school.
RESULTS
Of 120 students, 48 responded (40% response rate). Most respondents (92%)
believed AI would benefit patients diagnostically, and 94% felt AI would
support physicians in decision-making. Regarding specialty choice, 41.6%
reported decreased interest in certain fields due to AI, while 54.2% reported
no change. Interest in AI education was high: 64.5% expressed interest in
formal training, 21% were neutral, and 14.5% were not interested.
CONCLUSION
In this rural-oriented medical school sample, students broadly endorsed
AI’s diagnostic and clinical value. Although AI influenced specialty interest
for a subset of students, more than half reported no change in their career
preferences. Strong student support for integrating AI content into the
curriculum highlights the need to prepare future physicians for an AI-enabled
healthcare environment.
Presentation Tag(s)
Student Travel Award Winner, Student Presentation, Best Student Oral Nominee