Clinical Trials for Artificial Intelligence in Cancer Diagnosis: A Cross-Sectional Study of Registered Trials in ClinicalTrials.gov

Abstract

Objective: Clinical trials are the most effective way to judge the merits of diagnosis and treatment strategies. The in-depth mining of clinical trial data enables us to grasp the application trend of artificial intelligence (AI) for cancer diagnosis. The aim of this study was to analyze the characteristics of registered trials on AI for cancer diagnosis. Methods: Clinical trials on AI for cancer diagnosis registered on the ClinicalTrials.gov database were searched and downloaded. Statistical analysis was performed by using SPSS 20.0 software. Results: A total of 97 registered trials were included. Of them, only 27 (27.8%) were interventional trials and 70 (72.1%) were observational trials. Fifteen (15.4%) trials had been completed. Fifty trials were in recruitment, and another 18 remained unrecruited. The number of cases included in the clinical trials tended to be large, 31 (32.0%) trials including samples ranging from 100 to 499 cases and 17 (17.5%) trials including samples ranging from 500 to 999 cases. Of the 27 interventional trials, only two trials reported trials' phase. Most (85.2%) interventional trials were for diagnosis, and a few (3.7%) were for the purpose of both the diagnosis and therapy of cancers. For the observational clinical trials, 46 (65.7%) were cohort studies, and 11 (15.7%) were case-only studies. Among the observational trials, 46 (65.7%) were prospective studies and 13 (18.6%) were retrospective studies. Among 97 trials, 37 (38.1%) involved colorectal cancer, 11 (11.3%) involved breast cancer, 43 (44.3%) were for imaging diagnosis, 33 (34.0%) were for endoscopic diagnosis, and 11 (11.3%) were for pathological diagnosis. For the interventional trials, 11 trials were parallel assignment (40.7%), and 14 were single group assignment (51.9%). Among the 27 interventional trials, 18 (66.7%) trials were performed without masking, 6 (22.2%) trials were performed with single masking, only 1 (3.7%) was performed with double masking, and 2 (7.4%) was performed with triple masking. Conclusion: It appears that most registered trials on AI for cancer diagnosis are observational design, and more trials are needed in this field.

Registered Trials on Artificial Intelligence Conducted in Emergency Department and Intensive Care Unit: A Cross-Sectional Study on ClinicalTrials.gov

“…For status, 23 (37.70%) were Characteristics of Trials at ICU Table 5 shows the characteristics of trials on AI conducted in emergency department. Among the 85 trials, 48 (56.47%) were interventional trials, and 37 (43.53%) were observational A total of 146 registered trials were identified, including 61 trials in ED and 85 in ICU, which is similar with our previous study for cancer (33). Over half trials registered after 2017, and it was consistent with the development of industry 4.0, which depended on AI to empower medicine (37).…”

“…A total of 146 registered trials were identified, including 61 trials in ED and 85 in ICU, which is similar with our previous study for cancer ( 33 ). Over half trials registered after 2017, and it was consistent with the development of industry 4.0, which depended on AI to empower medicine ( 37 ).…”

“…Pre-registered in public registries is the most important strategy to ensure transparency ( 29 ) and now been required for all trials by The International Committee of Medical Journal Editors (ICMJE). Thus, analyzing registered trials will know the progress in such field, and many studies have been published to analyze registered trials in Clinicaltrials.gov , such as acupuncture ( 30 ), ventilator-associated pneumonia (VAP) ( 31 ), old populations with infectious diseases ( 32 ), and cancer diagnosis ( 33 ). However, there is no such study for AI in ED and ICU.…”

Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine

“…The 2012 ImageNet Large Scale Visual Recognition Competition (ILSVRC) competition for object recognition rates, which featured a major breakthrough in large-scale GPU-based deep learning (led by Dr. Jeffrey Hinton's research team on Alex-net) [30], and Google's announcement in the same year of its success in recognizing a cat from YouTube images using deep learning, led to a renewed interest in artificial intelligence research around the world [31]. AI technology is actively used in the medical field as well, and its effectiveness has been demonstrated in various results such as medical image analysis and omics analysis [1,[32][33][34][35][36][37][38][39]. In the field of oncology, the introduction of AI is also being actively studied in tumor screening, including lung and breast cancer [40,41].…”

Machine learning in clinical decision making

“…7 Numerous studies are increasingly incorporating ML algorithms across a large variety of domains. [8][9][10] As ML decision-support systems are gradually entering the clinics, it is important to carefully evaluate their utility and performance while realizing their limitations. Importantly, the know-how of these platforms should be expanded from computational biologists to the clinical end users, who should be informed and made aware of the trade-offs between these factors 11 in determining the performance and accuracy of ML pipelines in various clinical contexts.…”