Transforming Medical Education: Cultivating Statistical Thinking in the AI Era
Abstract
Artificial intelligence (AI) is rapidly transforming healthcare and medical education. Strong statistical thinking skills are vital for evaluating and applying AI tools. However, traditional medical statistics education has not adapted to this demand. This paper first analyzes the connotation and importance of statistical thinking, points out the significant challenges currently faced by medical statistics education, and then proposes strategies such as innovative teaching methods combined with evidence-based medicine, utilizing AI platforms for supplemental teaching, multidisciplinary integration, and strengthening the understanding of the statistical foundations of AI to enhance the statistical thinking abilities of medical professionals. This study emphasizes the importance of cultivating medical statistical thinking in the era of AI to improve the quality of medical education and ensure the safety and effectiveness of future medical services.
References
Joseph G, Bhatti N, Mittal R, et al., 2025, Current Application and Future Prospects of Artificial Intelligence in Healthcare and Medical Education: A Review of Literature. Cureus, 17(1): e77313.
Alowais SA, Alghamdi SS, Alsuhebany N, et al., 2023, Revolutionizing Healthcare: The Role of Artificial Intelligence in Clinical Practice. BMC Medical Education, 23(1): 689.
Vora LK, Gholap AD, Jetha K, et al., 2023, Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design. Pharmaceutics, 15(7): 1916.
Bhuyan SS, Sateesh V, Mukul N, et al., 2025, Generative Artificial Intelligence Use in Healthcare: Opportunities for Clinical Excellence and Administrative Efficiency. Journal of Medical Systems, 49(1): 10.
Kamalov F, Santandreu Calonge D, Gurrib I, 2023, New Era of Artificial Intelligence in Education: Towards a Sustainable Multifaceted Revolution. Sustainability, 15(16): 12451.
Hamilton A, 2024, Artificial Intelligence and Healthcare Simulation: The Shifting Landscape of Medical Education. Cureus, 16(5): e59747.
Charow R, Jeyakumar T, Younus S, et al., 2021, Artificial Intelligence Education Programs for Health Care Professionals: Scoping Review. JMIR Medical Education, 7(4): e31043.
Seyyed-Kalantari L, Zhang H, McDermott MBA, et al., 2021, Underdiagnosis Bias of Artificial Intelligence Algorithms Applied to Chest Radiographs in Under-Served Patient Populations. Nature Medicine, 27(12): 2176–2182.
Tong C, 2019, Statistical Inference Enables Bad Science; Statistical Thinking Enables Good Science. American Statistician, 73(sup1): 246–261.
Carver R, Everson M, Gabrosek J, et al., 2016, Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report 2016, Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report.
De A, 2023, Statistical Thinking, Systematic Reviews, And Meta-Analyses For Biomarkers and Medical Tests, Alexandria, VA.
Hunter DJ, Holmes C, 2023, Where Medical Statistics Meets Artificial Intelligence. New England Journal of Medicine, 389(13): 1211–1219.
Obermeyer Z, Powers B, Vogeli C, et al., 2019, Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations. Science, 366(6464): 447–453.
Rawal A, Raglin A, Rawat DB, et al., 2025, Causality for Trustworthy Artificial Intelligence: Status, Challenges and Perspectives. ACM Comput. Surv., 57(6): 146:1–146:30.
Gazquez-Garcia J, Sánchez-Bocanegra CL, Sevillano JL, 2025, AI in the Health Sector: Systematic Review of Key Skills for Future Health Professionals. JMIR Medical Education, 11: e58161.
Morgan DJ, Pineles L, Owczarzak J, et al., 2021, Accuracy of Practitioner Estimates of Probability of Diagnosis Before and After Testing. JAMA Internal Medicine, 181(6): 747–755.
Rao AA, Awale M, Davis S, 2023, Medical Diagnosis Reimagined as a Process of Bayesian Reasoning and Elimination. Cureus, 15(9): e45097.
Norori N, Hu Q, Aellen FM, et al., 2021, Addressing Bias in Big Data and AI for Health Care: a Call for Open Science. Patterns, 2(10): 100347.
Koçak B, Ponsiglione A, Stanzione A, et al., 2024, Bias in Artificial Intelligence for Medical Imaging: Fundamentals, Detection, Avoidance, Mitigation, Challenges, Ethics, and Prospects. Diagnostic and Interventional Radiology, 31(2): 75–88.
Sarno LD, Caroselli A, Tonin G, et al., 2024, Artificial Intelligence in Pediatric Emergency Medicine: Applications, Challenges, and Future Perspectives. Biomedicines, 12(6): 1220.
Miles S, Price GM, Swift L, et al., 2010, Statistics Teaching in Medical School: Opinions of Practising Doctors. BMC Medical Education, 10(1): 75.
Li C, Zhang Y, Qin W, et al., 2024, Assessing Attitudes Towards Biostatistics Education Among Medical Students: Adaptation and Preliminary Evaluation of the Chinese Version Survey of Attitudes Towards Statistics (SATS-36). BMC Medical Education, 24: 634.
Korolkiewicz M, Fewster-Young N, Marmolejo-Ramos F, et al., 2024, Fear of the Unknown: Relationship Between Statistics Anxiety and Attitudes Toward Statistics of University Students in Three Countries. Teaching Statistics, 47(1): 17–38.
Terry J, Field A, 2024, A Systematic Review of Theories Explaining the Relationship Between Statistics Anxiety and Statistical Literacy, OSF.
Nowacki AS, Brearley AM, Oster RA, et al., 2024, Diagnosing Statistical Education Needs of Health Science Learners. Journal of Statistics and Data Science Education, 2024: 1–11.
Agrawal A, Sharma A, Sharma A, et al., 2024, Challenges Faced by Medical Faculty in Implementation of Competency-Based Medical Education and Lessons Learned. Journal of Education and Health Promotion, 13: 345.
MacDougall M, Cameron HS, Maxwell SRJ, 2019, Medical Graduate Views on Statistical Learning Needs for Clinical Practice: A Comprehensive Survey. BMC Medical Education, 20(1): 1.
Grainge MJ, 2023, Teaching Medical Statistics Within the Context of Evidence Based Medicine, Farnell DJJ, Medeiros Mirra R, Teaching Biostatistics in Medicine and Allied Health Sciences, Springer International Publishing, Cham, 19–29.
Huang Y, Gomaa A, Semrau S, et al., 2023, Benchmarking ChatGPT-4 on ACR Radiation Oncology In-Training (TXIT) Exam and Red Journal Gray Zone Cases: Potentials and Challenges for AI-Assisted Medical Education and Decision Making in Radiation Oncology. Front Oncol, 13: 1265024.
Hammour AA, Hammour KA, Alhamad H, et al., 2024, Exploring Jordanian Medical Students’ Perceptions and Concerns about ChatGPT in Medical Education: A Cross-Sectional Study. Journal of Pharmaceutical Policy and Practice, 17(1): 2429000.
Ai Z, Yu M, Feng T, et al., 2017, The Analysis of Case-Based Learning in the Teaching Effectiveness of Medical Statistics. Chinese Journal of Medical Education, 37(6): 875–881.
Bruen C, Illing J, Daly R, et al., 2025, Medical Student Experiences of Case-Based Learning (CBL) at a Multicultural Medical School. BMC Medical Education, 25(1): 152.
Bland JM, 2004, Teaching Statistics to Medical Students Using Problem-Based Learning: The Australian Experience. BMC Medical Education, 4: 31.
Rodríguez-Martín I, Condés E, Sánchez-Gómez J, et al., 2024, Perceptions of Co-Teaching as a Pedagogical Approach to Integrate Basic and Clinical Sciences. Frontiers in Medicine, 11.
Mao Y, Xiao J, Zhu L, et al., 2025, The Basic Statistical Concepts and Their Interrelationships in Diagnostic Research. Postgraduate Medical Journal, 101(1193): 263–267.
Jamshidian M, Jamshidian P, 2023, Teaching Statistical Inference Through a Conceptual Lens: A Spin on Existing Methods with Examples. Journal of Statistics and Data Science Education, 32(1): 54–72.
Sapci AH, Sapci HA, 2020, Artificial Intelligence Education and Tools for Medical and Health Informatics Students: Systematic Review. JMIR Medical Education, 6(1): e19285.
Gilson A, Safranek CW, Huang T, et al., 2023, How Does ChatGPT Perform on the United States Medical Licensing Examination (USMLE)? The Implications of Large Language Models for Medical Education and Knowledge Assessment. JMIR Medical Education, 9: e45312.
Xu X, Chen Y, Miao J, 2024, Opportunities, Challenges, and Future Directions of Large Language Models, Including ChatGPT in Medical Education: A Systematic Scoping Review. Journal of Educational Evaluation for Health Professions, 21: 6.
Jhajj KS, Jindal P, Kaur K, 2024, Use of Artificial Intelligence Tools for Research by Medical Students: A Narrative Review. Cureus, 16(3): e55367.
Sedlakova J, Stanikić M, Gille F, et al., 2025, Refining Established Practices for Research Question Definition to Foster Interdisciplinary Research Skills in a Digital Age: Consensus Study with Nominal Group Technique. JMIR Medical Education, 11(1): e56369.
Youm J, Christner J, Hittle K, et al., 2024, The 6 Degrees of Curriculum Integration in Medical Education in the United States. Journal of Educational Evaluation for Health Professions, 21.
Yoon D, 2022, Preparing for a New World: Making Friends with Digital Health. Yonsei Medical Journal, 63(Suppl): S108.
Zsidai B, Hilkert A, Kaarre J, et al., 2023, A Practical Guide to the Implementation of AI in Orthopaedic Research – Part 1: Opportunities in Clinical Application and Overcoming Existing Challenges. Journal of Experimental Orthopaedics, 10(1): 117.
Lee KH, Lee RW, Lee KH, et al., 2023, The Development and Validation of an AI Diagnostic Model for Sacroiliitis: A Deep-Learning Approach. Diagnostics, 13(24): 3643.
Wang CY, 2024, Visual Error Patterns in Multi-Modal AI: A Statistical Approach. arXiv. https://doi.org/10.48550/arXiv.2412.00083
Liaw W, Kueper JK, Lin S, et al., 2022, Competencies for the Use of Artificial Intelligence in Primary Care. Annals of Family Medicine, 20(6): 559–563.
