[Proposal for Responsible Use of Generative Artificial Intelligence in Medical Practice].

IF 0.8 4区 医学 Q4 CLINICAL NEUROLOGY
David A Pérez Martínez
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引用次数: 0

Abstract

Introduction: The advancement of artificial intelligence (AI), particularly generative AI, has significantly transformed the field of medicine, impacting healthcare delivery, medical education, and research. While the opportunities are substantial, the implementation of AI also raises important ethical and technical challenges, including risks related to data bias, the potential erosion of clinical skills, and concerns about information privacy.

Development: AI has demonstrated great potential in optimizing both clinical and educational processes. However, its operation based on probabilistic prediction is inherently prone to errors and biases. Healthcare professionals must be aware of these limitations and advocate for a transparent, responsible, and safe integration of AI, while maintaining full ethical and legal responsibility for clinical decisions. It is essential to safeguard traditional clinical competencies and prioritize the use of AI in automating low-value, repetitive tasks. In biomedical research, transparency and independent validation are crucial to ensure the reproducibility of findings. Similarly, in medical education, structured training in AI is vital to enable professionals to apply these tools safely and effectively in clinical practice.

Conclusions: Generative AI offers a transformative potential for medicine, but its adoption must be guided by rigorous ethical standards. Comprehensive training, risk mitigation, and the preservation of core clinical skills are essential pillars for its responsible implementation. This transformation must be led by the medical profession to ensure a patient-centered approach to care.

Abstract Image

Abstract Image

[关于在医疗实践中负责任地使用生成式人工智能的建议]。
导读:人工智能(AI)的进步,特别是生成式人工智能,已经显著改变了医学领域,影响了医疗保健服务、医学教育和研究。虽然机会巨大,但人工智能的实施也带来了重要的道德和技术挑战,包括与数据偏见、临床技能潜在侵蚀以及对信息隐私的担忧相关的风险。发展:人工智能在优化临床和教育过程方面显示出巨大的潜力。然而,其基于概率预测的操作本身就容易出现错误和偏差。医疗保健专业人员必须意识到这些局限性,并倡导透明、负责任和安全的人工智能集成,同时对临床决策保持完全的道德和法律责任。必须保护传统的临床能力,并优先使用人工智能来自动化低价值、重复的任务。在生物医学研究中,透明度和独立验证对于确保研究结果的可重复性至关重要。同样,在医学教育中,人工智能的结构化培训对于使专业人员能够在临床实践中安全有效地应用这些工具至关重要。结论:生成式人工智能为医学提供了变革的潜力,但它的采用必须以严格的道德标准为指导。全面培训、降低风险和保留核心临床技能是负责任实施的重要支柱。这种转变必须由医疗行业主导,以确保以患者为中心的护理方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Revista de neurologia
Revista de neurologia 医学-临床神经学
CiteScore
2.50
自引率
8.30%
发文量
117
审稿时长
3-8 weeks
期刊介绍: Revista de Neurología fomenta y difunde el conocimiento generado en lengua española sobre neurociencia, tanto clínica como experimental.
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