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人工智能在生物医学研究与实践中应扮演持久的角色吗?

Should Artificial Intelligence Play a Durable Role in Biomedical Research and Practice?

作者信息

Bongrand Pierre

机构信息

Laboratory Adhesion and Inflammation (LAI), Inserm UMR 1067, Cnrs Umr 7333, Aix-Marseille Université UM 61, 13009 Marseille, France.

出版信息

Int J Mol Sci. 2024 Dec 13;25(24):13371. doi: 10.3390/ijms252413371.

DOI:10.3390/ijms252413371
PMID:39769135
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11676049/
Abstract

During the last decade, artificial intelligence (AI) was applied to nearly all domains of human activity, including scientific research. It is thus warranted to ask whether AI thinking should be durably involved in biomedical research. This problem was addressed by examining three complementary questions (i) What are the major barriers currently met by biomedical investigators? It is suggested that during the last 2 decades there was a shift towards a growing need to elucidate complex systems, and that this was not sufficiently fulfilled by previously successful methods such as theoretical modeling or computer simulation (ii) What is the potential of AI to meet the aforementioned need? it is suggested that recent AI methods are well-suited to perform classification and prediction tasks on multivariate systems, and possibly help in data interpretation, provided their efficiency is properly validated. (iii) Recent representative results obtained with machine learning suggest that AI efficiency may be comparable to that displayed by human operators. It is concluded that AI should durably play an important role in biomedical practice. Also, as already suggested in other scientific domains such as physics, combining AI with conventional methods might generate further progress and new applications, involving heuristic and data interpretation.

摘要

在过去十年中,人工智能(AI)已应用于人类活动的几乎所有领域,包括科学研究。因此,有必要探讨人工智能思维是否应持久地参与生物医学研究。通过研究三个互补的问题来解决这个问题:(i)生物医学研究人员目前遇到的主要障碍是什么?有人认为,在过去20年中,对阐明复杂系统的需求日益增长,而诸如理论建模或计算机模拟等以前成功的方法并不能充分满足这一需求。(ii)人工智能满足上述需求的潜力有多大?有人认为,最近的人工智能方法非常适合对多变量系统执行分类和预测任务,并可能有助于数据解释,前提是其效率得到适当验证。(iii)最近通过机器学习获得的代表性结果表明,人工智能的效率可能与人类操作员相当。得出的结论是,人工智能应在生物医学实践中持久发挥重要作用。此外,正如在物理学等其他科学领域中已经提出的那样,将人工智能与传统方法相结合可能会产生进一步的进展和新的应用,包括启发式方法和数据解释。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ed19/11676049/fc4bf1ff4a5e/ijms-25-13371-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ed19/11676049/4900fadf0aef/ijms-25-13371-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ed19/11676049/fc4bf1ff4a5e/ijms-25-13371-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ed19/11676049/4900fadf0aef/ijms-25-13371-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ed19/11676049/fc4bf1ff4a5e/ijms-25-13371-g002.jpg

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