The role of natural language processing in improving cancer care: A scoping review with narrative synthesis

Abstract

OBJECTIVES: To review studies of Natural Language Processing (NLP) systems that assist in cancer care, explore use cases and summarise current research progress. METHODS: A scoping review, searching six databases (1) MEDLINE, (2) Embase, (3) IEEE Xplore, (4) ACM Digital Library, (5) Web of Science, and (6) ACL Anthology. Studies were included that reported NLP systems that had been used to improve cancer management by patients or clinicians. Studies were synthesised descriptively and using content analysis. RESULTS: Twenty-nine studies were included. Studies mainly applied NLP in mixed cancer types (n = 10, 34.48 %) and breast cancer (n = 8, 27.59 %). NLP was used in four main ways: (1) to support patient education and self-management; (2) to improve efficiency in clinical care by summarising, extracting, and categorising data, and supporting record-keeping; (3) to support prevention and early detection of patient problems or cancer recurrence; and (4) to improve cancer treatment by supporting clinicians to make evidence-based treatment decisions. Studies highlighted a wide variety of use cases for NLP technologies in cancer care. However, few technologies have been evaluated within clinical settings, none have been evaluated against clinical outcomes, and none have been implemented into clinical care. CONCLUSION: NLP has the potential to improve cancer care via several mechanisms, including information extraction and classification, which could enable automation and personalization of care processes. Additionally, NLP tools such as chatbots show promise in improving patient communication and support. However, there are deficiencies in the evaluation and clinical integration challenges. Interdisciplinary collaboration between computer scientists and clinicians will be essential if NLP technologies are to fulfil their potential to improve patient experience and outcomes. Registered Protocol: https://doi.org/10.17605/OSF.IO/G9DSR.

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