Purpose of review 

Artificial intelligence (AI) is a transformative technology that has the potential to improve and augment the clinical workflow in supportive and palliative care (SPC). The objective of this study was to provide an overview of the recent studies applying AI to SPC in cancer patients.

Recent findings 

Between 2020 and 2022, 29 relevant studies were identified and categorized into two applications: predictive modeling and text screening. Predictive modeling uses machine learning and/or deep learning algorithms to make predictions regarding clinical outcomes. Most studies focused on predicting short-term mortality risk or survival within 6 months, while others used models to predict complications in patients receiving treatment and forecast the need for SPC services. Text screening typically uses natural language processing (NLP) to identify specific keywords, phrases, or documents from patient notes. Various applications of NLP were found, including the classification of symptom severity, identifying patients without documentation related to advance care planning, and monitoring online support group chat data.

Summary 

This literature review indicates that AI tools can be used to support SPC clinicians in decision-making and reduce manual workload, leading to potentially improved care and outcomes for cancer patients. Emerging data from prospective studies supports the clinical benefit of these tools; however, more rigorous clinical validation is required before AI is routinely adopted in the SPC clinical workflow.

中文翻译：

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人工智能在癌症患者支持和姑息治疗中的应用进展

审查目的 

人工智能(AI) 是一种变革性技术，有可能改善和增强支持和姑息治疗(SPC) 中的临床工作流程。本研究的目的是概述最近将 AI 应用于癌症患者 SPC 的研究。

最近的发现 

2020 年至 2022 年间，确定了 29 项相关研究并将其分为两个应用：预测建模和文本筛选。预测建模使用机器学习和/或深度学习算法来预测临床结果。大多数研究侧重于预测短期死亡风险或 6 个月内的存活率，而其他研究则使用模型来预测接受治疗的患者的并发症并预测对 SPC 服务的需求。文本筛选通常使用自然语言处理(NLP) 从患者笔记中识别特定的关键字、短语或文档。发现了 NLP 的各种应用，包括症状严重程度的分类、识别没有与预先护理计划相关的文件的患者，以及监控在线支持群聊数据。

概括 

这篇文献综述表明，AI 工具可用于支持 SPC 临床医生进行决策并减少人工工作量，从而可能改善癌症患者的护理和结果。来自前瞻性研究的新数据支持这些工具的临床益处；然而，在 SPC 临床工作流程中常规采用 AI 之前，需要进行更严格的临床验证。