Turkish Journal of Biology
Abstract
Background/aim: Artificial intelligence (AI), particularly large language models (LLMs) such as ChatGPT and DeepSeek, is being increasingly applied in clinical care, research, and education. The aim of this review is to examine how these tools may transform the conduct of medical and biological research and to define their limitations.
Materials and methods: A narrative synthesis of the literature was performed, encompassing studies published between 2020 and 2025. Peer-reviewed journals, systematic reviews, and high-impact original research articles were included to ensure an evidence-based overview. The principle applications, validation metrics, and clinical implications across orthopedics, oncology, cardiology, internal medicine, and the biological sciences were analyzed.
Results: LLMs demonstrate strong potential in supporting physicians during clinical decision-making, enhancing patient education, and assisting researchers in their work. They are valuable for language-related tasks and for generating structured, clear, and comprehensible content. However, concerns persist regarding data privacy, algorithmic bias, factual accuracy, and excessive dependence on data-driven outputs. Responsible implementation requires safeguards such as human oversight, model transparency, and domain-specific training.
Conclusion: AI tools such as ChatGPT, DeepSeek, and similar models are transforming the way healthcare is delivered and studied. Their current capabilities appear highly promising. However, clinicians, technical experts, and policymakers must collaborate to ensure the safe, equitable, effective, and ethical integration of these technologies into real-world healthcare workflows.
Author ORCID Identifier
MAHMUT ENES KAYAALP: 0000-0002-9545-7454
ONUR GÜLTEKİN: 0000-0002-0836-076X
SERHAT AKÇAALAN: 0000-0001-7350-6422
HAMİT ÇAĞLAYAN KAHRAMAN: 0000-0001-6150-9300
HÜSEYİN NEVZAT TOPÇU: 0009-0007-2272-0462
GÜLŞAH KAVRUL KAYAALP: 0000-0001-7490-7076
DOI
10.55730/1300-0152.2765
Keywords
Artificial intelligence, large language models, ChatGPT, DeepSeek, clinical decision support, medical education
First Page
585
Last Page
599
Publisher
The Scientific and Technological Research Council of Türkiye (TÜBİTAK)
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
KAYAALP, M, GÜLTEKİN, O, AKÇAALAN, S, KAHRAMAN, H, TOPÇU, H, & KAVRUL KAYAALP, G (2025). Artificial intelligence in medical and biological research: promise and perils of ChatGPT and DeepSeek in advancing healthcare. Turkish Journal of Biology 49 (5): 585-599. https://doi.org/10.55730/1300-0152.2765
