Artificial intelligence in medical and biological research: promise and perils of ChatGPT and DeepSeek in advancing healthcare

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.

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