Artificial intelligence (AI) is poised to revolutionize how science, and biomedical research in particular, are done. With AI, problem solving and complex tasks using massive data sets can be performed at a much higher rate and dimensionality level compared to humans. With the ability to handle huge data sets and self-learn, AI is already being exploited in drug design, drug repurposing, toxicology, and material identification. AI could also be used in both basic and clinical research in study design, defining outcomes, analyzing data, interpreting findings, and even identifying the most appropriate areas of investigation and funding sources. State-of-the-art AI-based large language models (LLM), such as ChatGPT and Perplexity, are positioned to change forever how science is communicated and how scientists interact with one another and their profession, including post-publication appraisal and critique. Like all revolutions, upheaval will follow and not all outcomes can be predicted, necessitating guardrails at the onset, especially to minimize the untoward impact of the many drawbacks of LLMs, which include lack of confidentiality, risk of hallucinations, and propagation of mainstream albeit potentially mistaken opinions and perspectives. In this review, we highlight areas of biomedical research that are already being reshaped by AI and how AI is likely to impact it further in the near future. We discuss the potential benefits of AI in biomedical research and address possible risks, some surrounding the creative process, that warrant further reflection.

中文翻译：

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探索人工智能在生物医学研究和临床实践中的前景和挑战。

人工智能 (AI) 有望彻底改变科学，特别是生物医学研究的进行方式。与人类相比，借助人工智能，使用海量数据集解决问题和复杂任务可以以更高的速率和维度水平执行。凭借处理海量数据集和自学习的能力，人工智能已经在药物设计、药物再利用、毒理学和材料识别中得到应用。人工智能还可以用于基础研究和临床研究的研究设计、定义结果、分析数据、解释结果，甚至确定最合适的调查领域和资金来源。最先进的基于人工智能的大语言模型 (LLM)，例如 ChatGPT 和 Perplexity，将永远改变科学的传播方式以及科学家之间及其职业的互动方式，包括发表后评估和批评。像所有革命一样，剧变将会随之而来，并且并非所有结果都可以预测，因此需要在一开始就采取护栏，特别是为了最大限度地减少法学硕士许多缺点的不良影响，其中包括缺乏保密性、产生幻觉的风险以及主流的传播（尽管有可能）错误的意见和观点。在这篇综述中，我们重点介绍了人工智能已经重塑的生物医学研究领域，以及人工智能在不久的将来可能对其产生进一步影响的方式。我们讨论了人工智能在生物医学研究中的潜在好处，并解决了可能的风险，其中一些风险围绕着创意过程，值得进一步反思。