Current status of artificial intelligence colonoscopy on improving adenoma detection rate based on systematic review of multiple metanalysis

Table 1 Summary of metanalysis included in this systemic review

Ref.	Study design	Year of publication	Number of study and participants	Type of the study	Overall heterogeneity
Soleymanjahi et al[25]	Comparison of CADe-versus conventional colonoscopy performance	2024	33 studies, 27404 participants	Mixed	I² = 74%
Makar et al[17]	Impact of CADe systems on key colonoscopy quality indicators	2024	28 RCTs, 23861 participants	RCT	I² = 48%
Lee et al[15]	Evaluation of how study characteristics influence outcomes in AI-assisted polyp detection	2024	24 RCTs, 17413 participants	RCT	I² = 53%
Patel et al[26]	Assessment of benefits and harms associated with CADe in real-world colonoscopy	2024	8 studies, m9782 participants	Non-RCT	I² = 83%
Lou et al[18]	Prospective advantages and disadvantages of AI-assistance systems in colonoscopy	2024	12 studies, 11660 participants	Non-RCT	I² = 87%
Barua et al[27]	Comparison of ADR with and without AI utilization	2023	33 RCTs, 27404 participants	RCT	I² = 38.33%
Mehta et al[28]	Effectiveness of CADs in early colorectal cancer diagnosis compared to conventional colonoscopy	2023	15 studies, 174602 participants	Mixed	Not mentioned
Shiha et al[29]	Effectiveness of CADe in adenoma and polyp detection rates	2023	12 RCTs, 11340 participants	RCT	I2 = 64%
Zhang et al[30]	Accuracy measurement of AI-assisted colonoscopy	2023	8 RCTs, 2984 participants	RCT	Moderate to high heterogeneity
Nazarian et al[31]	Utilizing CADs for polyp detection and characterization	2023	13 RCTs, 15334 participants	RCT	I2 = 86%
Adiwinata et al[32]	Impact of AI colonoscopyon increasing ADR	2023	13 studies, 2958 participants	Mixed	I2 = 57%
Vadhwana et al[33]	Assessment of AI colonoscopy in real-time histological prediction	2023	80 studies, 25304 participants	RCT	Moderate to high heterogeneity
Hassan et al[34]	Summary of RCTs on CADe systems for colorectal neoplasia detection	2021	28 studies, 29079 participants	Mixed (RCTs and preclinical studies)	I2 = 42.1%
Lui et al[35]	AI's role in histology prediction and colorectal polyp detection	2021	10 RCTs, 6629 participants	RCT	I2 = 38.33%
Huang et al[36]	Evaluation of AI's impact on colonoscopy outcome metrics	2021	5 studies, 4311 participants	RCT	I2 = 36%
Li et al[37]	Evaluation of AI's effect on ADR	2021	26 RCT, 17413 participants	Mixed	I2 = 39.2%
Wang et al[38]	AI-assisted polyp detection and classification	2021	6 RCTs, 5058 participants	RCT	I2 = 69%
Ashat et al[39]	Determining the statistical significance of AI polyp detection for clinical adoption	2021	6 RCTs, 4996 participants	RCT	I2 = 28%
Deliwala et al[40]	Comparison of colorectal cancer detection between standard and AI-assisted colonoscopies	2021	5 RCTs, 4354 participants	RCT	I2 = 70%
Hassan et al[41]	Diagnostic accuracy of CADe systems in colorectal neoplasia detection	s2020	5 RCTs, 4311 Participants	RCT	I2 = 42%
Wei et al[42]	Analysis of CADe's effect on ADR and adenoma detection reproducibility	2020	18 studies, 969318 participants	Mixed	I2 = 91%
Mohan et al[43]	Comparison of ADR between CADe assisted colonoscopy and standard colonoscopy	2020	6 RCTs, 4962 participants	RCT	I2 = 56%

ADR: Adenoma detection rate; CADe: Computer assistance detection examination; RCT: Randomized control trail; AI: Artificial intelligence.