Artificial intelligence in medical and biological research: promise and perils of ChatGPT and DeepSeek in advancing healthcare
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View abstract on PubMed
Summary
This summary is machine-generated.Artificial intelligence (AI), specifically large language models (LLMs), shows promise in medical research and clinical care. Responsible implementation requires addressing data privacy, bias, and accuracy concerns through human oversight and transparency.
Area Of Science
- Medical and Biological Research
- Clinical Applications of AI
Background
- Artificial intelligence (AI), particularly large language models (LLMs), is increasingly utilized in clinical care, research, and education.
- These advanced AI tools are transforming healthcare delivery and scientific inquiry.
Purpose Of The Study
- To review the transformative potential of AI and LLMs in medical and biological research.
- To define the limitations and challenges associated with implementing these technologies in healthcare.
Main Methods
- A narrative synthesis of literature published between 2020 and 2025.
- Inclusion of peer-reviewed journals, systematic reviews, and high-impact original research.
- Analysis of applications, validation metrics, and clinical implications across various medical specialties and biological sciences.
Main Results
- LLMs show significant potential in clinical decision support, patient education, and research assistance.
- AI excels in language-based tasks, generating structured and clear content.
- Key concerns include data privacy, algorithmic bias, factual accuracy, and over-reliance on AI outputs.
Conclusions
- AI tools like ChatGPT and DeepSeek offer promising capabilities for healthcare and research.
- Safe, equitable, and ethical integration necessitates collaboration among clinicians, experts, and policymakers.
- Human oversight, model transparency, and domain-specific training are crucial for responsible AI implementation.
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