Table 1.

Study characteristics^a

Study	No.	Institution	Cancer diagnosis	Enrollment methodology	AI source	AI methodology	Algorithm runtime	Conclusion
Alexander et al. (14)	102	Peter MacCallum Cancer Center, Melbourne, Australia	Lung	Clinical data extracted from study database and medical records to match patients to 10 phase I-III cancer clinical trials on clinicaltrials.gov at local cancer center	Watson for Clinical Trial Matching (WCTM), developed by IBM	Trial data intake was optimized with 3 rounds of trial ingestion by NLP before matching patients to clinical trials based on primary cancer staging, metastatic disease, performance status, mutations, prior cancer therapy, lung surgery type, cancer histology, demographics, echocardiography, pathology, past medical history, medications, comorbidities	15.5 s	The AI-based clinical trial matching system allows efficient and reliable screening of cancer patients for clinical trials
Beck et al. (15)	239	Highlands Oncology Group, Arkansas, USA	Breast	Structured and unstructured patient data were included to assess clinical trials eligibility to 4 breast cancer trials listed on clinicaltrials.gov at local cancer center	Watson for Clinical Trial Matching (WCTM), developed by IBM	Trial data intake was optimized with 3 rounds of trial intake before used to match patients to clinical trials based on structured patient data (laboratory tests, sex, cancer diagnosis, age) and unstructured data sources (most recent medical progress note)	24 min, which is 78% reduction compared with manual screening	Clinical trial matching system displayed a promising performance in screening patients with breast cancer for trial eligibility
Calaprice-Whitty et al. (16)	48 124	Comprehensive Blood and Cancer Center, Bakersfield, CA, USA	Breast, lung	Structured and unstructured medical records evaluated to identify eligible patients retrospectively in 3 completed trials at local cancer center	Mendel.ai, developed by Mendel	Text recognition system to extract text from scanned medical documents, clinical language understanding, and entailment system to read output of text recognition system and its meanings, knowledge-based ontology and wisdom system to synthesize data to data dictionary	20 h for group 1 compared with 19 d for manual screening 1.15 h for group 2 compared with 263 d for manual screening	Augmentation of human resources with artificial intelligence could yield sizable improvements over standard practices in several aspects of the patient prescreening process, as well as in approaches to feasibility, site selection, and trial selection
Cesario et al. (17)	96	Comprehensive Cancer Center, Roma, Italy	Breast, lung	Digital research assistant via progressive web app identifies patients eligible for a clinical trial of all those conducted at the cancer center	Digital Research Assistant, developed in-house	AI-based models using age, immunophenotype, genetics, histology, BMI, stage of therapy	NR	Might represent a valid research tool supporting clinicians and scientists to optimize the enrollment of patients in clinical trials
Cuggia et al. (18)	285	Centre Eugene Marquis, Rennes, France	Prostate	Automatic selection of clinical trials eligibility criteria (national research project) to retrospectively identify patients discussed in multidisciplinary meetings for clinical trial eligibility, for eligibility of 4 clinical trials conducted at cancer center	Computerized recruitment support system, developed as a French national research project	Computerized recruitment support system based on semantic web approach	NR	System was scalable to other clinical domains
Delorme et al. (19)	264	Gustave Roussy Cancer Campus, Villejuif, France	All	Free text consultation reports evaluated to identify eligible patients retrospectively included in phase I or II oncology trials	Model developed in-house	Natural language preprocessing pipeline to turn free text into numerical features for random forest model	NR	Machine learning with semantic conservation is a promising tool to assist physicians in selecting patients prone to achieve successful screening and dose-limiting toxicity period completion in early phase oncology clinical trials
Haddad et al. (20)	318	Mayo Clinic, Rochester, MN, USA	Breast	Structured and unstructured medical records evaluated to identify eligible patients for 4 breast cancer trials listed on clinicaltrials.gov at local cancer center	Watson for Clinical Trial Matching (WCTM), developed by IBM	NLP to identify cancer stage, cancer subtype, genetic markers, prior cancer therapy, surgical status, pathology, therapy-related characteristics	NR	Accurately exclude ineligible patients and offer potential to increase screening efficiency and accuracy
Meystre et al. (21)	229	Hollings Cancer Center, Charleston, SC, USA	Breast	Clinical notes assessed to assess eligibility for 3 breast cancer clinical trials at local cancer center	Model developed in-house	Named entity recognition task based on sequential token-based labeling using a support vector machine retrieved clinical notes, extracted eligibility criteria	NR	Can be used to extract eligibility criteria from HER clinical notes and automatically discover patients possibly eligible for a clinical trial with good accuracy, which could be leveraged to reduce the workload of humans screening patients for trials
Ni et al. (22)	NR	Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA	All	Demographics and notes processed to evaluate eligibility to all 70 clinical trials at local cancer center	Model developed in-house	NLP and information extraction of demographics, diagnoses, clinical notes	1 min, which saves 346 min of manpower	Could dramatically increase trial screening efficiency of oncologists and enable participation of small practices, which are often left out from trial enrollment
Zeng et al. (23)	NR	MD Anderson Cancer Center, Houston, TX, USA	All	Genetic textual document repositories and matching documents assessed to evaluate eligibility for 153 preprocessed potential targeted therapy clinical trials from clinicaltrials.gov and MD Anderson clinical trial database	Model developed in-house	Genetic textual document repository to identify 1 of 543 genes whose molecular abnormality can be detected on sequencing panels	NR	NLP tool was generalizable; tool may partially automate process of information gathering

^aAI = artificial intelligence; BMI = body mass index; NLP = natural language processing; NR = not reported.