人工智能在医学中的应用前景:意大利人工智能医学协会的观点。

Francesco Andrea Causio, Luigi DE Angelis, Giacomo Diedenhofen, Angelo Talio, Francesco Baglivo
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引用次数: 0

摘要

2023 年 12 月 7 日,意大利人工智能医学学会(Società Italiana Intelligenza Artificiale in Medicina,SIIAM)召开了第一届年会,标志着将人工智能(AI)融入意大利医疗保健框架的一个重要里程碑。本文报告了此次活动期间举办的合作研讨会,重点介绍了来自医学、工程学、数据科学和法律等不同领域的 51 位专业人士的集体努力。与会者的跨学科背景在提出针对医疗挑战的创新型人工智能解决方案方面发挥了至关重要的作用。讨论的核心是几项旨在改善患者护理和简化医疗流程的人工智能应用。值得注意的是,大型语言模型(LLM)在慢性病患者远程监控中的应用成为了一个重点领域。这些模型有望通过详细的症状检查和异常检测加强对病人的监测,从而促进及时的医疗干预。另一项重要建议涉及利用 LLMs 提高医疗沟通中的同理心,以应对文化多样性和医疗保健专业人员的高压力水平所带来的挑战。此外,会议还讨论了开发用于儿科急诊室标准化治疗的机器学习算法,以及在农村医疗环境中加强人工智能应用的教育举措的必要性。研讨会还探讨了如何利用 LLMs 高效提取和分析科学文献中的数据、解释医疗保健规范以及简化出院记录。本文全面概述了研讨会上提出的想法和解决方案,反映了与会者的前瞻性思维和人工智能在医疗保健领域的变革潜力。
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Perspectives on AI use in medicine: views of the Italian Society of Artificial Intelligence in Medicine.

The first annual meeting of the Italian Society for Artificial Intelligence in Medicine (Società Italiana Intelligenza Artificiale in Medicina, SIIAM) on December 7, 2023, marked a significant milestone in integrating artificial intelligence (AI) into Italy's healthcare framework. This paper reports on the collaborative workshop conducted during this event, highlighting the collective efforts of 51 professionals from diverse fields including medicine, engineering, data science, and law. The interdisciplinary background of the participants played a crucial role in generating ideas for innovative AI solutions tailored to healthcare challenges. Central to the discussions were several AI applications aimed at improving patient care and streamlining healthcare processes. Notably, the use of Large Language Models (LLMs) in remote monitoring of chronic patients emerged as an area of focus. These models promise enhanced patient monitoring through detailed symptom checking and anomaly detection, thereby facilitating timely medical interventions. Another significant proposal involved employing LLMs to improve empathy in medical communication, addressing the challenges posed by cultural diversity and high-stress levels among healthcare professionals. Additionally, the development of Machine Learning algorithms for standardizing treatment in pediatric emergency departments was discussed, along with the need for educational initiatives to enhance AI adoption in rural healthcare settings. The workshop also explored using LLMs for efficient data extraction and analysis in scientific literature, interpreting healthcare norms, and streamlining hospital discharge records. This paper provides a comprehensive overview of the ideas and solutions proposed at the workshop, reflecting the participants' forward-thinking vision and the potential of AI to revolutionize healthcare.

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