探索AI在医学研究和知识合成中的使用困境:深度研究工具的视角
Ariel Yuhan Ong1,2,3, David A Merle1,2,3, Siegfried K Wagner1,2,3
1UCL Institute of Ophthalmology, Institute of Ophthalmology, University College London, 11-43 Bath St, London, EC1V 9EL, United Kingdom, +44 7482213973.
Journal of medical Internet research
|July 15, 2025
概括
像深度研究这样的人工智能 (AI) 工具可以快速合成科学文献,但对生物医学研究提出了担忧. 经验评估表明,人工智能使知识获取民主化,但人类智慧对于批判性评估和基于证据的实践至关重要.
科学领域:
- 生物医学研究的研究.
- 人工智能的人工智能是人工智能.
- 科学文献综合的科学文献综合.
背景情况:
- 人工智能 (AI) 正在改变科学研究,像OpenAI的深度研究这样的工具为文献审查提供了潜力.
- 虽然人工智能提供了速度和可访问性,但人们对引用完整性,批判性评估和科学思维存在担忧,特别是在生物医学研究中.
研究的目的:
- 实证地评估OpenAI的深度研究工具.
- 探索AI在科学文献合成中的能力和局限性.
- 思考人工智能对研究培训和基于证据的实践的影响.
主要方法:
- 结构化的实验,以评估深度研究的有效性.
- 对人工智能在合成科学文献方面的表现进行分析.
- 对科学诚信的更广泛影响进行定性反思.
主要成果:
- 深度研究在文献综合方面表现出了显著的能力.
- 确定了关键缺陷,包括对引用完整性和批判性评估的潜在影响.
- 人工智能工具使知识获取民主化,但不能取代人类的批判性思维.
结论:
- 人工智能工具在访问和合成科学信息方面提供了显著的优势.
- 批判性评价和深刻的科学思维仍然是人类必不可少的技能.
- 人工智能在研究中的整合需要仔细考虑其对基于证据的实践和培训的影响.
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