Randomized Controlled Trials of Artificial Intelligence in Clinical Practice: Systematic Review

被引:48
作者
Lam, Thomas Y. T. [1 ,2 ]
Cheung, Max F. K. [3 ]
Munro, Yasmin L. [3 ]
Lim, Kong Meng [3 ]
Shung, Dennis [4 ]
Sung, Joseph J. Y. [3 ,5 ]
机构
[1] Chinese Univ Hong Kong, Jockey Club Sch Publ Hlth & Primary Care, Hong Kong, Peoples R China
[2] Chinese Univ Hong Kong, Stanley Ho Big Data Decis Analyt Res Ctr, Hong Kong, Peoples R China
[3] Nanyang Technol Univ, Lee Kong Chian Sch Med, Singapore, Singapore
[4] Yale Sch Med, Dept Med Digest Dis, New Haven, CT USA
[5] Nanyang Technol Univ, Lee Kong Chian Sch Med, 11 Mandalay Rd, Singapore 308232, Singapore
关键词
artificial intelligence; randomized controlled trial; systematic review; clinical; gastroenterology; clinical informatics; mobile phone; COMPUTER-AIDED DETECTION; ADENOMA MISS RATE; COLONOSCOPY; MULTICENTER; NEOPLASIA;
D O I
10.2196/37188
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Background: The number of artificial intelligence (AI) studies in medicine has exponentially increased recently. However, there is no clear quantification of the clinical benefits of implementing AI-assisted tools in patient care.Objective: This study aims to systematically review all published randomized controlled trials (RCTs) of AI-assisted tools to characterize their performance in clinical practice. Methods: CINAHL, Cochrane Central, Embase, MEDLINE, and PubMed were searched to identify relevant RCTs published up to July 2021 and comparing the performance of AI-assisted tools with conventional clinical management without AI assistance. We evaluated the primary end points of each study to determine their clinical relevance. This systematic review was conducted following the updated PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. Results: Among the 11,839 articles retrieved, only 39 (0.33%) RCTs were included. These RCTs were conducted in an approximately equal distribution from North America, Europe, and Asia. AI-assisted tools were implemented in 13 different clinical specialties. Most RCTs were published in the field of gastroenterology, with 15 studies on AI-assisted endoscopy. Most RCTs studied biosignal-based AI-assisted tools, and a minority of RCTs studied AI-assisted tools drawn from clinical data. In 77% (30/39) of the RCTs, AI-assisted interventions outperformed usual clinical care, and clinically relevant outcomes improved with AI-assisted intervention in 70% (21/30) of the studies. Small sample size and single-center design limited the generalizability of these studies. Conclusions: There is growing evidence supporting the implementation of AI-assisted tools in daily clinical practice; however, the number of available RCTs is limited and heterogeneous. More RCTs of AI-assisted tools integrated into clinical practice are needed to advance the role of AI in medicine.
引用
收藏
页数:30
相关论文
共 61 条
[1]   Carbohydrate Counting App Using Image Recognition for Youth With Type 1 Diabetes: Pilot Randomized Control Trial [J].
Alfonsi, Jeffrey E. ;
Choi, Elizabeth E. Y. ;
Arshad, Taha ;
Sammott, Stacie-Ann S. ;
Pais, Vanita ;
Nguyen, Cynthia ;
Maguire, Bryan R. ;
Stinson, Jennifer N. ;
Palmert, Mark R. .
JMIR MHEALTH AND UHEALTH, 2020, 8 (10)
[2]   Augmented reality and artificial intelligence-based navigation during percutaneous vertebroplasty: a pilot randomised clinical trial [J].
Auloge, Pierre ;
Cazzato, Roberto Luigi ;
Ramamurthy, Nitin ;
de Marini, Pierre ;
Rousseau, Chloe ;
Garnon, Julien ;
Charles, Yan Philippe ;
Steib, Jean-Paul ;
Gangi, Afshin .
EUROPEAN SPINE JOURNAL, 2020, 29 (07) :1580-1589
[3]   Safety and Feasibility of the PEPPER Adaptive Bolus Advisor and Safety System: A Randomized Control Study [J].
Avari, Parizad ;
Leal, Yenny ;
Herrero, Pau ;
Wos, Marzena ;
Jugnee, Narvada ;
Arnoriaga-Rodriguez, Maria ;
Thomas, Maria ;
Liu, Chengyuan ;
Massana, Quim ;
Lopez, Beatriz ;
Nita, Lucian ;
Martin, Clare ;
Fernandez-Real, Jose Manuel ;
Oliver, Nick ;
Fernandez-Balsells, Merce ;
Reddy, Monika .
DIABETES TECHNOLOGY & THERAPEUTICS, 2021, 23 (03) :175-186
[4]   A Review of Applications of Machine Learning in Mammography and Future Challenges [J].
Batchu, Sai ;
Liu, Fan ;
Amireh, Ahmad ;
Waller, Joseph ;
Umair, Muhammad .
ONCOLOGY, 2021, 99 (08) :483-490
[5]   The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database [J].
Benjamens, Stan ;
Dhunnoo, Pranavsingh ;
Mesko, Bertalan .
NPJ DIGITAL MEDICINE, 2020, 3 (01)
[6]   DREAM5: An open-label, randomized, cross-over study to evaluate the safety and efficacy of day and night closed-loop control by comparing the MD-Logic automated insulin delivery system to sensor augmented pump therapy in patients with type 1 diabetes at home [J].
Biester, Torben ;
Nir, Judith ;
Remus, Kerstin ;
Farfel, Alon ;
Muller, Ido ;
Biester, Sarah ;
Atlas, Eran ;
Dovc, Klemen ;
Bratina, Natasa ;
Kordonouri, Olga ;
Battelino, Tadej ;
Philip, Moshe ;
Danne, Thomas ;
Nimri, Revital .
DIABETES OBESITY & METABOLISM, 2019, 21 (04) :822-828
[7]   Effect of Machine Learning on Dispatcher Recognition of Out-of-Hospital Cardiac Arrest During Calls to Emergency Medical Services A Randomized Clinical Trial [J].
Blomberg, Stig Nikolaj ;
Christensen, Helle Collatz ;
Lippert, Freddy ;
Ersboll, Annette Kjaer ;
Torp-Petersen, Christian ;
Sayre, Michael R. ;
Kudenchuk, Peter J. ;
Folke, Fredrik .
JAMA NETWORK OPEN, 2021, 4 (01)
[8]   Automated Diagnosis of Plus Disease in Retinopathy of Prematurity Using Deep Convolutional Neural Networks [J].
Brown, James M. ;
Campbell, J. Peter ;
Beers, Andrew ;
Chang, Ken ;
Ostmo, Susan ;
Chan, R. V. Paul ;
Dy, Jennifer ;
Erdogmus, Deniz ;
Ioannidis, Stratis ;
Kalpathy-Cramer, Jayashree ;
Chiang, Michael F. .
JAMA OPHTHALMOLOGY, 2018, 136 (07) :803-810
[9]   Deep Learning Computer-aided Polyp Detection Reduces Adenoma Miss Rate: A United States Multi-center Randomized Tandem Colonoscopy Study (CADeT-CS Trial) [J].
Brown, Jeremy R. Glissen ;
Mansour, Nabil M. ;
Wang, Pu ;
Chuchuca, Maria Aguilera ;
Minchenberg, Scott B. ;
Chandnani, Madhuri ;
Liu, Lin ;
Gross, Seth A. ;
Sengupta, Neil ;
Berzin, Tyler M. .
CLINICAL GASTROENTEROLOGY AND HEPATOLOGY, 2022, 20 (07) :1499-+
[10]   Prospective evaluation of an automated method to identify patients with severe sepsis or septic shock in the emergency department [J].
Brown S.M. ;
Jones J. ;
Kuttler K.G. ;
Keddington R.K. ;
Allen T.L. ;
Haug P. .
BMC Emergency Medicine, 16 (1)