Search

Current US lung cancer screening recommendations limit eligibility to adults with a pack‐year (PY) history of ≥20 years and the first 15 years since quit (YSQ). The authors conducted a systematic review to better understand lung cancer incidence, risk and mortality among otherwise eligible individuals in this population beyond 15 YSQ. The PubMed and Scopus databases were searched through February 14, 2023, and relevant articles were searched by hand. Included studies examined the relationship between adults with both a ≥20‐PY history and ≥15 YSQ and lung cancer diagnosis, mortality, and screening ineligibility. One investigator abstracted data and a second confirmed. Two investigators independently assessed study quality and certainty of evidence (COE) and resolved discordance through consensus. From 2636 titles, 22 studies in 26 articles were included. Three studies provided low COE of elevated lung cancer incidence beyond 15 YSQ, as compared with people who never smoked, and six studies provided moderate COE that the risk of a lung cancer diagnosis after 15 YSQ declines gradually, but with no clinically significant difference just before and after 15 YSQ. Studies examining lung cancer‐related disparities suggest that outcomes after 15 YSQ were similar between African American/Black and White participants; increasing YSQ would expand eligibility for African American/Black individuals, but for a significantly larger proportion of White individuals. The authors observed that the risk of lung cancer not only persists beyond 15 YSQ but that, compared with individuals who never smoked, the risk may remain significantly elevated for 2 or 3 decades. Future research of nationally representative samples with consistent reporting across studies is needed, as are better data from which to examine the effects on health disparities across different populations. [ABSTRACT FROM AUTHOR]

An increasing number of studies have reported using natural language processing (NLP) to assist observational research by extracting clinical information from electronic health records (EHRs). Currently, no standardized reporting guidelines for NLP‐assisted observational studies exist. The absence of detailed reporting guidelines may create ambiguity in the use of NLP‐derived content, knowledge gaps in the current research reporting practices, and reproducibility challenges. To address these issues, we conducted a scoping review of NLP‐assisted observational clinical studies and examined their reporting practices, focusing on NLP methodology and evaluation. Through our investigation, we discovered a high variation regarding the reporting practices, such as inconsistent use of references for measurement studies, variation in the reporting location (reference, appendix, and manuscript), and different granularity of NLP methodology and evaluation details. To promote the wide adoption and utilization of NLP solutions in clinical research, we outline several perspectives that align with the six principles released by the World Health Organization (WHO) that guide the ethical use of artificial intelligence for health. [ABSTRACT FROM AUTHOR]

An increasing number of studies have reported using natural language processing (NLP) to assist observational research by extracting clinical information from electronic health records (EHRs). Currently, no standardized reporting guidelines for NLP-assisted observational studies exist. The absence of detailed reporting guidelines may create ambiguity in the use of NLP-derived content, knowledge gaps in the current research reporting practices, and reproducibility challenges. To address these issues, we conducted a scoping review of NLP-assisted observational clinical studies and examined their reporting practices, focusing on NLP methodology and evaluation. Through our investigation, we discovered a high variation regarding the reporting practices, such as inconsistent use of references for measurement studies, variation in the reporting location (reference, appendix, and manuscript), and different granularity of NLP methodology and evaluation details. To promote the wide adoption and utilization of NLP solutions in clinical research, we outline several perspectives that align with the six principles released by the World Health Organization (WHO) that guide the ethical use of artificial intelligence for health., (© 2022 The Authors. Clinical and Translational Science published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)