Kosin Medical Journal

Review articles

Generative artificial intelligence and large language models in competency-based medical education: applications, challenges, and future directions: In Hwa Jeong, Heeyoung Kim, Hyunyong Hwang; Kosin Med J. 2026;41(2):114-125. Published online June 23, 2026; DOI: https://doi.org/10.7180/kmj.26.173

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Abstract PDF PubReader ePub: Competency-based medical education emphasizes formative assessment and longitudinal portfolio review; however, the resulting workload is increasingly difficult for faculty members to sustain. Generative artificial intelligence (GenAI) and large language models (LLMs), such as ChatGPT, Gemini, and Claude, offer opportunities to alleviate these burdens while enriching teaching and learning. This narrative review synthesizes peer-reviewed literature published between January 2023 and April 2026, retrieved from PubMed/MEDLINE, Scopus, Web of Science, Google Scholar, and Education Resources Information Center (ERIC), on the practical applications of GenAI and LLMs in medical education. Five core domains were identified: simulation of clinical reasoning, formative assessment and e-portfolio evaluation, support for self-directed learning, curriculum design, and communication skills training. Important challenges and ethical considerations are also discussed, including cognitive offloading and the potential erosion of critical thinking, hallucinations and threats to academic integrity, data privacy and institutional compliance concerns, and the risk of widening the digital divide. Future directions include multimodal LLMs and integrated clinical simulations, longitudinal competency tracking, faculty development and institutionalization of artificial intelligence (AI) literacy, standardized evaluation frameworks and regulatory guidance, and ambient AI in smart educational environments. When integrated thoughtfully, GenAI and LLMs may reduce faculty workload and enrich the learner experience while preserving educators’ authority over competency judgments and the humanistic core of medical education. Realizing this potential will require coordinated investment in faculty’s AI literacy, robust governance, equity-focused deployment, and rigorous validation of AI-enhanced educational tools.

The ethics of using artificial intelligence in medical research: Shinae Yu, Sang-Shin Lee, Hyunyong Hwang; Kosin Med J. 2024;39(4):229-237. Published online December 6, 2024; DOI: https://doi.org/10.7180/kmj.24.140

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19 Citations

The integration of artificial intelligence (AI) technologies into medical research introduces significant ethical challenges that necessitate the strengthening of ethical frameworks. This review highlights the issues of privacy, bias, accountability, informed consent, and regulatory compliance as central concerns. AI systems, particularly in medical research, may compromise patient data privacy, perpetuate biases if they are trained on nondiverse datasets, and obscure accountability owing to their “black box” nature. Furthermore, the complexity of the role of AI may affect patients’ informed consent, as they may not fully grasp the extent of AI involvement in their care. Compliance with regulations such as the Health Insurance Portability and Accountability Act and General Data Protection Regulation is essential, as they address liability in cases of AI errors. This review advocates a balanced approach to AI autonomy in clinical decisions, the rigorous validation of AI systems, ongoing monitoring, and robust data governance. Engaging diverse stakeholders is crucial for aligning AI development with ethical norms and addressing practical clinical needs. Ultimately, the proactive management of AI’s ethical implications is vital to ensure that its integration into healthcare improves patient outcomes without compromising ethical integrity.

Citations

Citations to this article as recorded by

Anemia in young women: determinants and artificial intelligence-based management approaches
Guttapalam Sireesha, D. Madhavi, A. M. Beulah, M. Niharika, C. Pradeepthi
Frontiers in Artificial Intelligence.2026;[Epub] CrossRef
SERS Meets Artificial Intelligence: A New Frontier in Cancer Diagnosis and Prognosis
Noor ul Huda, Rana Zaki Abdul Bari, Muhammad Abdullah Javed, Muhammad Naeem Kiani, Yongdong Jin
Analytical Chemistry.2026; 98(12): 8757. CrossRef
The onco-bariatric paradigm: a tri-phasic metabolic obesity framework for synergizing glucagon-like peptide-1 receptor agonists and metabolic bariatric surgery
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Kosin Medical Journal.2026; 41(1): 3. CrossRef
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High-Confidence Computing.2026; : 100415. CrossRef
A Modular Evaluation of AI-Assisted Clinical Documentation
Julien Delaunay, Maissaa Sarkis, Jordi Solé-Casals, Jordi Cusido
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Human-in-the-Loop Performance of LLM-Assisted Arterial Blood Gas Interpretation: A Single-Center Retrospective Study
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Journal of Clinical Medicine.2025; 14(18): 6676. CrossRef
Applications, Challenges, and Prospects of Generative Artificial Intelligence Empowering Medical Education: Scoping Review
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JMIR Medical Education.2025; 11: e71125. CrossRef
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Iranildo Lopes de Oliveira, Rivana Ferreira de Souza, João Victor de Amorim Batista, Iara Costa Silvano, Ana Caroline Rocha de Melo Leite, Rodolfo de Melo Nunes
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Ethical Integration of Artificial Intelligence in Nursing Research: An Evidence-based Practice Project from Saudi Arabia
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Original article

Capsule endoscopy in Kazakhstan: a multicenter clinical experience: Sang Jun Sohn, Kanat Batyrbekov, Ainura Galiakbarova, Laura Yerdaliyeva, Jamilya Kaibullayeva, Jeongwoo Ju, Haejin Lee, Yeoun Joo Lee; Kosin Med J. 2024;39(3):179-185. Published online July 26, 2024; DOI: https://doi.org/10.7180/kmj.24.116

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Abstract PDF PubReader ePub

Background
By analyzing small bowel capsule endoscopy (SBCE) performed in two large hospitals in Kazakhstan, we aimed to explore the characteristics of patients representative of Central Asia and the technical characteristics of SBCE.
Methods
SBCE cases were retrospectively analyzed. A descriptive analysis was conducted on the patients’ demographic data, diagnosis, and clinical symptoms. The results of SBCE, such as the lesions found, transit time and retention rate in the stomach and small bowel, and bowel visualization quality, were analyzed. Complications related to SBCE were investigated.
Results
SBCE was performed in 123 patients. Abdominal pain (81.3%) and chronic diarrhea (66.7%) were the most common symptoms, followed by weight loss (25.2%) and gastrointestinal bleeding (15.4%). The most common disease was Crohn’s disease (52.0%). Definite lesions, such as ulcers, polyps, and bleeding, were identified in 55.3% of patients. SBCE was successfully completed in all cases except for 11 (9.1%). The average small bowel transit time was 4 hours and 28 minutes. Excellent visualization (>75% of mucosa) was reported in 82.5% of patients. No patients experienced complications.
Conclusions
SBCE performed in Kazakhstan showed a high diagnostic yield with high-quality patient selection and no complications.

Citations

Citations to this article as recorded by

Exploring the impact of capsule endoscopy in Kazakhstan: a significant milestone
Jong Yoon Lee
Kosin Medical Journal.2024; 39(3): 151. CrossRef

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