癌症生存护理计划中的人工智能:除了诊断之外还有什么?

Soumya Jindal, Meemansa Jindal, Pooja Bhati
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引用次数: 0

摘要

随着世界范围内健康和生存水平的提高,幸存者护理经验似乎有了显著改善。然而,多重障碍在其制定和执行方面造成了不公平。基于人工智能的数字系统可以分析和优化多学科团队、治疗方案、沟通、预测和患者结果,此外还提供前所未有的速度、准确性和精确度。虽然大多数研究项目都集中在基于人工智能的检测系统上,但重要的是要了解人工智能在这些幸存者诊断后生活中的范围。本文对现有文献和将人工智能整合到癌症诊断后的生存护理中的研究工作进行了了解。方法:于2022年7月在PubMed数据库中使用关键词(“人工智能”[All Fields]或“人工智能”[MeSH Terms])和(“癌症幸存者护理”[All Fields]或“癌症幸存者”[MeSH Terms])进行文献检索,发现33篇已发表的英文文章。结果:通过我们的回顾,我们可以确定三个主题:“癌症幸存者护理中现有的漏洞是什么”,“人工智能目前如何解决这些漏洞”以及“人工智能和机器学习如何解决未来癌症幸存者护理中的这些障碍”。我们还发现了人工智能辅助数字系统的许多漏洞,如可移植性、可解释性、可靠性、有效性、数据保密性、所有权和责任、噪音和过度诊断、培训资源等。结论:根据达尔文的“适者生存”,人工智能将取代拒绝被授权的传统医生,而不是人工智能。认识到研究结果和迫切需要推进将创造一个更公平的幸存者护理交付在未来。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial Intelligence in cancer survivorship care plans: what lies beyond diagnostics?
With the worldwide rising standards of health and survival, survivorship care experiences appear to have improved significantly. However multiple barriers are causing inequities in its formulation and delivery. Artificial intelligence based digital systems can analyze and optimize multi-disciplinary teams, treatment options, communication, prognostication, and patient outcomes, besides offering unprecedented speed, accuracy, and precision. While most of the research projects are focused on AI-based detection systems, it is important to understand the scope of AI in the life of these survivors post-diagnosis. This paper provides an understanding of the existing literature and the research efforts being made toward integrating AI in cancer survivorship care after diagnosis. Method: A literature search on the PubMed database in July 2022 using keywords (("Artificial Intelligence"[All Fields] OR "Artificial Intelligence"[MeSH Terms]) AND ("Cancer survivorship care"[All Fields] OR "cancer survivor"[MeSH Terms])) revealed 33 articles published in English. Results: Through our review we could identify three themes: ‘What are the existing loopholes in cancer survivorship care’, ‘How is AI addressing them presently’ and ‘How can AI and ML address these barriers in cancer survivorship care in the future’. We also found many loopholes in AI-assisted digital systems such as transferability, explicability, reliability, validity, data confidentiality, ownership and responsibility, noise and overdiagnosis, resources for training etc. Conclusion: In accordance with Darwin’s "survival of the fittest", AI-empowered clinicians, not AI will replace the traditional ones who refuse to be empowered. Recognition of the research results and the imminent need to advance will create a more equitable survivorship care delivery in the future.
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