Artificial intelligence in colonoscopy: Enhancing quality indicators for optimal patient outcomes

Table 1 Take-home messages for practicing endoscopists based on current guidelines

Society	Current position on AI in colonoscopy
ESGE[16]	Supports CADe use to reduce AMR, provided minimal false positives and no WT prolongation; allows CADx for “leave-in-situ”/“resect and discard”, if accuracy is equivalent to experts. Recommends AI integration to help standardize performance in less experienced endoscopists
BMJ[17]	Recommends against routine CADe use due to concerns over overdiagnosis, minimal clinical benefit, and increased surveillance; recommendation graded as “weak”
AGA[19]	Does not issue a definitive recommendation for or against CADe use, citing uncertainty in long-term outcomes
WEO[20]	Acknowledges CADe/CADx potential but urges cost-effectiveness assessment; highlights the need for high-quality real-world studies across healthcare systems

AI: Artificial intelligence; ESGE: European Society of Gastrointestinal Endoscopy; AGA: American Gastroenterological Association; WEO: World Endoscopy Organization; CADe: Computer-aided detection; AMR: Adenoma miss rate; CADx: Computer-aided diagnosis; WT: Withdrawal time.

Table 2 Summary of systematic reviews and meta-analyses evaluating the impact of artificial intelligence on detection rate metrics in colonoscopy[18,37,40,41,43,44,46,48,50,51,55-61,108-113]

Ref.	Year	Study type	Patient number	Number of studies	Primary outcome	Artificial intelligence vs conventional colonoscopy (95%CI)
Barua et al[37]	2020	Systematic review and meta-analysis	4311	5 RCTs	ADR, PDR, APC, PPC, aAPC	ADR: 29.6% vs 19.3%; RR = 1.52 (1.31 to 1.77); PDR: 45.4% vs 30.6%; RR = 1.48 (1.37 to 1.60); No difference in aAPC; PPC: 0.93 vs 0.51; Mean difference 0.42 (0.33 to 0.50); APC: 0.41 vs 0.23; Mean difference 0.18 (0.13 to 0.22)
Aziz et al[108]	2020	Systematic review and meta-analysis	2815	3 RCTs	ADR	32.9% vs 20.8%; RR = 1.58 (1.39 to 1.80)
Mohan et al[109]	2020	Systematic review and meta-analysis	4962	6 RCTs	ADR	32.8% vs 21.1%; RR = 1.5 (1.30 to 1.72)
Ashat et al[110]	2021	Systematic review and meta-analysis	5058	6 RCTs	ADR	33.7% vs 22.9%; OR = 1.76 (1.55 to 2.00)
Hassan et al[56]	2021	Systematic review and meta-analysis	4354	5 RCTs	ADR	36.6% vs 25.2%; RR = 1.44 (1.27 to 1.62)
Deliwala et al[61]	2021	Systematic review and meta-analysis	4996	6 RCTs	ADR, PDR	ADR: OR = 1.77 (1.50 to 2.08); PDR: OR = 1.91 (1.68 to 2.16)
Nazarian et al[51]	2021	Systematic review and meta-analysis	5577 subjects for polyp detection with ADR and PDR	48 studies (18 studies for polyp detection, 22 studies for polyp characterization and 8 studies for PDR)	ADR, PDR, polyp characterization	PDR: OR = 1.75 (1.56 to 1.96); ADR: OR = 1.53 (1.32 to 1.77)
Li et al[59]	2021	Systematic review and meta-analysis	4311	5 studies	ADR, PDR	PDR: OR = 1.91 (1.68 to 2.16); ADR: OR = 1.75 (1.52 to 2.01)
Zhang et al[60]	2021	Systematic review and meta-analysis	5427	7 RCTs	ADR, PDR	PDR: OR = 1.95 (1.75 to 2.19); ADR: OR = 1.72 (1.52 to 1.95)
Spadaccini et al[48]	2021	Systematic review and network meta-analysis	34445	50 RCTs	ADR	CADe vs HD white-light endoscopy OR = 1.78 (1.44 to 2.18); CADe vs chromoendoscopy OR = 1.45 (1.14 to 1.85); CADe vs increased mucosal visualization systems OR = 1.54 (1.22 to 1.94)
Huang et al[41]	2022	Systematic review and meta-analysis	6629	10 RCTs	ADR, PDR	ADR: 35.4% vs 24.9%; RR = 1.43 (1.33 to 1.53)/OR = 1.45 (1.32 to 1.59); PDR: 48.6% vs 33.8%; RR = 1.44 (1.35 to 1.53)/OR = 1.90 (1.70 to 2.11)
Shah et al[62]	2023	Systematic review and meta-analysis	10928	14 RCTs	ADR, PDR	ADR: OR = 1.52 (1.39 to 1.67); PDR: OR = 1.48 (1.37 to 1.61)
Hassan et al[57]	2023	Systematic review and meta-analysis	18232	21 RCTs	ADR, APC, aAPC, number of serrated lesions/colonoscopy, number of polypectomies for nonneoplastic lesions, WT	ADR: 44.0% vs 35.9%; RR = 1.24 (1.16 to 1.33)
Lou et al[58]	2023	Systematic review and meta-analysis	27404	33 RCTs	ADR, APC	ADR: RR = 1.242 (1.159 to 1.332); APC: IRR = 1.390 (1.277 to 1.513)
Adiwinata et al[50]	2023	Systematic review and meta-analysis	NA	13 RCTs	ADR, PDR	PDR: OR = 1.46 (1.13 to 1.89); ADR: OR = 1.58 (1.37 to 1.82)
Shiha et al[43]	2023	Systematic review and meta-analysis	11340	12 RCTs	ADR	41.4% vs 33%; RR = 1.26 (1.18 to 1.35)
Wei et al[111]	2024	Systematic review and meta-analysis	11660	12 studies	ADR	ADR was statistically significantly higher with AI vs CC (36.3% vs 35.8%, RR = 1.13, 95%CI: 1.01 to 1.28)
Aziz et al[112]	2024	Systematic review and network meta-analysis	61172	94 RCTs	ADR	Autofluorescence imaging: RR = 1.33 (1.06 to 1.66); Dye-based chromoendoscopy: RR = 1.32 (1.17 to 1.50); Endo cuff: RR = 1.19 (1.04 to 1.35); Endo cuff vision: RR = 1.26 (1.13 to 1.41); Endoring: RR = 1.30 (1.10 to 1.52); Flexible spectral imaging color enhancement: RR = 1.26 (1.09 to 1.46); Full-spectrum endoscopy: RR = 1.40 (1.19 to 1.65); High definition: RR = 1.41 (1.28 to 1.54); Linked color imaging: RR = 1.21 (1.08 to 1.36); Narrow band imaging: RR = 1.33 (1.18 to 1.48); Water exchange: RR = 1.22 (1.06 to 1.42); Water immersion: RR = 1.47 (1.19 to 1.82)
Soleymanjahi et al[18]	2024	Systematic review and meta-analysis	36201	44 RCTs	APC, ACN	APC: 0.98 vs 0.78; IRD = 0.22 (0.16 to 0.28); ACN: 0.16 vs 0.15; IRD = 0.01 (-0.01 to 0.02)
Patel et al[46]	2024	Systematic review and meta-analysis	9782	8 non-randomized studies	ADR	44% vs 38%; RR = 1.11 (0.97 to 1.28)
Gangwani et al[113]	2024	Network meta-analysis	22560	26 studies (20 RCTs, 3 retrospective, 3 prospective studies)	ADR	AI vs single operator: RR = 1.1 (0.9 to 1.2)
Lee et al[44]	2024	Systematic review and meta-analysis	17413	24 RCTs	ADR	Tandem studies: RR = 1.18 (1.08 to 1.30); Parallel studies: RR = 1.26 (1.17 to 1.35); Overall ADR: RR = 1.24 (1.17 to 1.31)
Makar et al[40]	2025	Systematic review and meta-analysis	23861	28 RCTs	ADR	ADR: RR = 1.20 (1.14 to 1.27)
Spadaccini et al[55]	2025	Systematic review and meta-analysis	5421	10 RCTs	ADR	0.62 vs 0.52; RR = 1.19 (1.08 to 1.31)

CI: Confidence interval; RCTs: Randomized clinical trials; ADR: Adenoma detection rate; PDR: Polyp detection rate; NR: Not reported; APC: Adenomas per colonoscopy; PPC: Mean number of polyps per colonoscopy; aAPC: Mean number of advanced adenomas detected per colonoscopy; WT: Withdrawal time; ACN: Advanced colorectal neoplasia; RR: Relative risk; OR: Odds ratio; HD: High definition; CADe: Computer-aided detection; AI: Artificial intelligence; IRR: Incidence rate ratio; CC: Conventional colonoscopy; IRD: Incidence rate difference.

Table 3 Summary of systematic reviews and meta-analyses evaluating the impact of artificial intelligence on miss rate metrics in colonoscopy[40,52,57,58]

Ref.	Year	Study type	Patient number	Number of studies	Primary outcome	Artificial intelligence vs conventional colonoscopy (95%CI)
Hassan et al[57]	2023	Systematic review and meta-analysis	18232	21 RCTs	AMR	AMR: 16% vs 35%; RR = 0.45 (0.35 to 0.58)
Lou et al[58]	2023	Systematic review and meta-analysis	27404	33 RCTs	AMR	AMR: RR = 0.495 (0.390 to 0.627)
Maida et al[52]	2024	Systematic review and meta-analysis	1718	6 RCTs	AMR, PMR	PMR: 16.3% vs 38.1%; RR = 0.44 (0.33 to 0.60); AMR: 15.3% vs 34.1%; RR = 0.46 (0.38 to 0.55)
Makar et al[40]	2025	Systematic review and meta-analysis	23861	28 RCTs	AMR	AMR: RR = 0.45 (0.37 to 0.54)

CI: Confidence interval; RCTs: Randomized clinical trials; AMR: Adenoma miss rate; PMR: Polyp miss rate; RR: Relative risk.

Table 4 Summary of study characteristics evaluating artificial intelligence performance for invasion depth prediction and polyp characterization in colonoscopy

Ref.	Year	Study type	Image type	AI algorithm	Patient number training set	Patient number validation set	Patient number testing set	Primary outcome	Sensitivity (%)	Specificity (%)	Accuracy (%)
Luo et al[69]	2021	Single center retrospective	WLI	Deep learning	556		137	Invasion depth Tis/T1a vs T1b/> T2	91.2	91	91.1
Minami et al[70]	2022	Single center retrospective	WLI, NBI, CCE	Deep learning	91	49	56	Submucosal invasion depth SM1 vs SM2/3	87.2	35.7	74.4
Lu et al[68]	2022	Multicenter retrospective	WLI, NBI, BLI	Deep learning	305		140	Invasion depth LGD/HGD/IM/SM1 vs SM2/advanced CRC	90.0	94.2	93.8
Nemoto et al[72]	2023	Multicenter retrospective	WLI	Deep learning	1084		400	Invasion depth Tis/T1a vs T1b	59.8	94.4	87.3
Tokunaga et al[73]1	2021	Single center retrospective	WLI	Deep learning	824		211	Invasion depth LGD/HGD/SM1 vs SM2/advanced CRC	96.7	75.0	90.3
Nakajima et al[71]	2022	Multicenter retrospective	WLI	Deep learning	313		44	Invasion depth Tis/T1a vs T1b	81.0	87.0	84.0
Song et al[75]	2020	Single center retrospective	NBI	Deep learning	624		545	Invasion depth SSP/BA/SM1 vs SM2/3	58.8	93.3	81.3
Lui et al[74]	2019	Single center retrospective	WLI, NBI	Deep learning	1652		76	Invasion depth polyps ≥ 2 cm adenoma/SM1 vs SM2	94.6	92.3	94.3
Yao et al[76]	2023	Multicenter retrospective	WLI, IEE	Deep learning	339		198	Invasion depth large SSPs ≥ 10 mm	78.8	96.2	90.4
Racz et al[66]	2022	Single center retrospective	NBI	Machine learning	279			Polyp characterization non-neoplastic vs neoplastic2	92.2	77.6	86.6
Ham et al[67]	2025	Single center retrospective	WLI	Deep learning	2696		476	Polyp characterization low vs high-risk adenomas ≤ 10 mm3	75.6	95.7	93.8

¹Clinical impact data were only explicitly reported in the study, which demonstrated a 24% reduction in unnecessary resections. Other included studies did not provide quantifiable real-world outcome metrics.

²Non-neoplastic polyps were defined as hyperplastic polyps, whereas neoplastic polyps included tubular adenomas, villous adenomas, and sessile serrated lesions.

³High-risk adenomas were defined as high-grade dysplasia or adenomas with villous histology.

AI: Artificial intelligence; WLI: White light imaging; NBI: Narrow band imaging; CCE: Conventional chromoendoscopy; BLI: Blue light imaging; IEE: Image enhanced endoscopy; SM: Submucosal invasion; LGD: Low-grade dysplasia; HGD: High-grade dysplasia; IM: Intramucosal carcinoma; CRC: Colorectal cancer; SSP: Sessile serrated polyp; BA: Benign conventional adenoma.

Table 5 Practical tips for incorporating artificial intelligence into endoscopy training

Practical tips for trainees using AI in colonoscopy
Use AI as a complement, not a replacement for clinical judgment
Review false positive alerts to learn distinguishing features
Combine AI with feedback from supervisors
Practice with and without AI assistance
Incorporate AI into video-based self-review
Be aware of potential deskilling with overuse
Participate in structured training programs including AI modules

AI: Artificial intelligence.