โก Quick Summary
This review explores the role of artificial intelligence (AI), particularly large language models (LLMs) like ChatGPT and DeepSeek, in transforming medical and biological research. While these tools show great promise in enhancing clinical decision-making and patient education, concerns regarding data privacy and algorithmic bias remain significant.
๐ Key Details
- ๐ Study Period: 2020 to 2025
- ๐ Sources: Peer-reviewed journals, systematic reviews, and high-impact original research articles
- โ๏ธ Applications: Orthopedics, oncology, cardiology, internal medicine, and biological sciences
- ๐ Focus: Validation metrics and clinical implications of AI tools
๐ Key Takeaways
- ๐ค AI tools like ChatGPT and DeepSeek are increasingly utilized in clinical care and research.
- ๐ก LLMs support physicians in clinical decision-making and enhance patient education.
- ๐ Language-related tasks are a strong suit for these AI models, generating clear and structured content.
- โ ๏ธ Concerns include data privacy, algorithmic bias, and factual accuracy.
- ๐ Responsible implementation requires human oversight and model transparency.
- ๐ Collaboration among clinicians, technical experts, and policymakers is essential for ethical integration.
- ๐ The future of AI in healthcare appears promising, with significant potential for improving patient outcomes.
๐ Background
The integration of artificial intelligence into healthcare is rapidly evolving, with tools like ChatGPT and DeepSeek leading the charge. These technologies promise to enhance the efficiency and effectiveness of medical and biological research, but they also raise important questions about their limitations and ethical implications. Understanding both the potential benefits and the risks associated with AI is crucial for its successful application in healthcare.
๐๏ธ Study
This review synthesizes literature from various studies published between 2020 and 2025, focusing on the applications of AI in different medical fields. By analyzing peer-reviewed articles and systematic reviews, the authors aimed to provide an evidence-based overview of how LLMs can transform research and clinical practices.
๐ Results
The findings indicate that LLMs have a strong potential to assist healthcare professionals in clinical decision-making and patient education. They excel in language-related tasks, producing structured and comprehensible content. However, the study also highlights ongoing concerns regarding data privacy, algorithmic bias, and the need for human oversight to ensure accuracy and ethical use of these technologies.
๐ Impact and Implications
The integration of AI tools like ChatGPT and DeepSeek could significantly change the landscape of healthcare delivery and research. By improving clinical decision-making and enhancing patient education, these technologies have the potential to lead to better patient outcomes. However, it is essential to address the ethical and practical challenges associated with their use to ensure that they are implemented safely and equitably.
๐ฎ Conclusion
The review underscores the transformative potential of AI in healthcare, particularly through tools like ChatGPT and DeepSeek. While the current capabilities of these technologies are promising, it is vital for stakeholders to collaborate in addressing the challenges they present. Ensuring a responsible and ethical integration of AI into healthcare workflows will be crucial for maximizing its benefits and minimizing risks.
๐ฌ Your comments
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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.
Author: [‘Kayaalp ME’, ‘Gรผltekin O’, ‘Akรงaalan S’, ‘Kahraman Hร’, ‘Topรงu HN’, ‘Kavrul Kayaalp G’]
Journal: Turk J Biol
Citation: Kayaalp ME, et al. Artificial intelligence in medical and biological research: promise and perils of ChatGPT and DeepSeek in advancing healthcare. Artificial intelligence in medical and biological research: promise and perils of ChatGPT and DeepSeek in advancing healthcare. 2025; 49:585-599. doi: 10.55730/1300-0152.2765