With the advent of artificial intelligence (AI), new diagnostic tools have emerged, potentially offering more consistent and accurate detection. This study aimed to systematically evaluate the diagnostic performance of AI in identifying pressure injuries (PIs).
Approach:
This systematic review and meta-analysis was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and was registered in Prospective Register of Systematic Reviews (CRD 42024618716). Comprehensive literature searches were performed across PubMed, Embase, IEEE, Cochrane, arXiv, and ACM databases for studies published up to November 2024. Two independent reviewers screened studies, extracted data, and assessed methodological quality using the QUADAS-AI (Quality Assessment of Diagnostic Accuracy Studies–Artificial Intelligence) tool. Statistical analyses were performed using R 4.3.3, RevMan 5.4, Stata 17, and Meta-Disc 1.4.
Results:
A total of 16 studies met the inclusion criteria, among which 12 provided 147 2 × 2 confusion matrices for quantitative synthesis. The meta-analysis showed a pooled sensitivity of 0.77 (95% CI: 0.76–0.77), specificity of 0.92 (95% CI: 0.92–0.92), and an area under the summary receiver operating characteristic curve of 0.928 (SE = 0.0079), indicating high diagnostic accuracy. Methodological quality was generally fair, but most studies were retrospective and lacked external validation.
Innovations:
This study applied the QUADAS-AI tool for quality assessment and conducted subgroup analyses by PIs stage, algorithm type, and region to offer a nuanced understanding of AI diagnostic performance.
Conclusions:
AI-based systems demonstrate promising diagnostic accuracy in detecting PIs, with high sensitivity and specificity.
MervisJS, PhillipsTJ. Pressure ulcers: Pathophysiology, epidemiology, risk factors, and presentation. J Am Acad Dermatol, 2019; 81(4):881–890; doi: 10.1016/j.jaad.2018.12.069
2.
RodgersK, SimJ, CliftonR. Systematic review of pressure injury prevalence in Australian and New Zealand hospitals. Collegian, 2021; 28(3):310–323; doi: 10.1016/j.colegn.2020.08.012
3.
XuY, ZhaoH, WuS, et al.Prediction models for intraoperative acquired pressure injury of adults: A systematic review and critical appraisal. Adv Wound Care (New Rochelle), 2025; doi: 10.1089/wound.2024.0238
4.
TrozicI, FischerL, DeckertS, et al.Impact of the degree of synergy between patient and nurse perceptions on the clinical outcome of pressure injury prevention: A mixed-methods systematic review protocol. BMJ Open, 2024; 14(9):e080542; doi: 10.1136/bmjopen-2023-080542
5.
SongYP, ShenHW, CaiJY, et al.The relationship between pressure injury complication and mortality risk of older patients in follow-up: A systematic review and meta-analysis. Int Wound J, 2019; 16(6):1533–1544; doi: 10.1111/iwj.13243
6.
SimJ, WilsonV, TuqiriK. The pressure injury prevalence and practice improvements (PIPPI) study: A multiple methods evaluation of pressure injury prevention practices in an acute-care hospital. Int Wound J, 2024; 21(10):e70050; doi: 10.1111/iwj.70050
7.
AnandP, KrankerK, ChenAY. Estimating the hospital costs of inpatient harms. Health Serv Res, 2019; 54(1):86–96; doi: 10.1111/1475-6773.13066
8.
KottnerJ, CuddiganJ, CarvilleK, et al.Prevention and treatment of pressure ulcers/injuries: The protocol for the second update of the international Clinical Practice Guideline 2019. J Tissue Viability, 2019; 28(2):51–58; doi: 10.1016/j.jtv.2019.01.001
9.
HillierB, ScandrettK, CoombeA, et al.Accuracy and clinical effectiveness of risk prediction tools for pressure injury occurrence: An umbrella review. PLoS Med, 2025; 22(2):e1004518; doi: 10.1371/journal.pmed.1004518
10.
CharalambousC, KouloriA, VasilopoulosA, et al.Evaluation of the validity and reliability of the waterlow pressure ulcer risk assessment scale. Med Arch, 2018; 72(2):141–144; doi: 10.5455/medarh.2018.72.141-144
11.
TusarMH, FayyazbakhshF, ZendehdelN, et al.AI-powered image-based assessment of pressure injuries using you only look once (YOLO) Version 8 models. Adv Wound Care (New Rochelle), 2025; doi: 10.1089/wound.2024.0245
12.
AlvesJ, AzevedoR, MarquesA, et al.Pressure injury prediction in intensive care units using artificial intelligence: A scoping review. Nurs Rep, 2025; 15(4):126; doi: 10.3390/nursrep15040126
13.
DeoRC. Machine learning in medicine. Circulation, 2015; 132(20):1920–1930; doi: 10.1161/circulationaha.115.001593
14.
XuHL, GongTT, LiuFH, et al.Artificial intelligence performance in image-based ovarian cancer identification: A systematic review and meta-analysis. EClinicalMedicine, 2022; 53:101662; doi: 10.1016/j.eclinm.2022.101662
15.
LiuX, DouY, GuoL, et al.A novel technique for rapid determination of pressure injury stages using intelligent machine vision. Geriatr Nurs, 2025; 61:98–105; doi: 10.1016/j.gerinurse.2024.10.046
16.
YeZ, ZhangD, ZhaoY, et al.Deep learning algorithms for melanoma detection using dermoscopic images: A systematic review and meta-analysis. Artif Intell Med, 2024; 155:102934; doi: 10.1016/j.artmed.2024.102934
17.
PeiJ, GuoX, TaoH, et al.Machine learning-based prediction models for pressure injury: A systematic review and meta-analysis. Int Wound J, 2023; 20(10):4328–4339; doi: 10.1111/iwj.14280
18.
ZhouY, YangX, MaS, et al.A systematic review of predictive models for hospital-acquired pressure injury using machine learning. Nurs Open, 2023; 10(3):1234–1246; doi: 10.1002/nop2.1429
19.
DweekatOY, LamSS, McGrathL. Machine learning techniques, applications, and potential future opportunities in pressure injuries (bedsores) management: A systematic review. Int J Environ Res Public Health, 2023; 20(1):796; doi: 10.3390/ijerph20010796
20.
SounderajahV, AshrafianH, RoseS, et al.A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: QUADAS-AI. Nat Med, 2021; 27(10):1663–1665; doi: 10.1038/s41591-021-01517-0
21.
LiuH, HuJ, ZhouJ, et al.Application of deep learning to pressure injury staging. J Wound Care, 2024; 33(5):368–378; doi: 10.12968/jowc.2024.33.5.368
22.
PageMJ, McKenzieJE, BossuytPM, et al.The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. Bmj, 2021; 372:n71; doi: 10.1136/bmj.n71
23.
StroupDF, BerlinJA, MortonSC, et al.Meta-analysis of observational studies in epidemiology: A proposal for reporting. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group. JAMA, 2000; 283(15):2008–2012; doi: 10.1001/jama.283.15.2008
24.
SenCK, DeMazumderD. Getting started on artificial intelligence in health care and clinical research: Includes Rigor Checklist for authors and reviewers. Adv Wound Care (New Rochelle), 2025; doi: 10.1177/21621918251380217
25.
DefloorT, ClarkM, WitherowA, et al.; European Pressure Ulcer Advisory Panel. EPUAP statement on prevalence and incidence monitoring of pressure ulcer occurrence. J Tissue Viability, 2005; 15(3):20–27; doi: 10.1016/s0965-206x(05)53004-3
26.
TescherA, DeppischM, MunroC, et al.Perioperative pressure injury prevention: National Pressure Injury Advisory Panel root cause analysis toolkit 3.0. J Wound Care, 2022; 31(Sup12):S4–S9; doi: 10.12968/jowc.2022.31.Sup12.S4
27.
WoodL, EggerM, GluudLL, et al.Empirical evidence of bias in treatment effect estimates in controlled trials with different interventions and outcomes: Meta-epidemiological study. Bmj, 2008; 336(7644):601–605; doi: 10.1136/bmj.39465.451748.AD
28.
AyB, TasarB, UtluZ, et al.Deep transfer learning-based visual classification of pressure injuries stages. Neural Comput & Applic, 2022; 34(18):16157–16168; doi: 10.1007/s00521-022-07274-6
29.
FergusP, ChalmersC, HendersonW, et al.Pressure ulcer categorization and reporting in domiciliary settings using deep learning and mobile devices: A clinical trial to evaluate end-to-end performance. Ieee Access, 2023; 11:65138–65152; doi: 10.1109/access.2023.3289839
30.
IkutaK, FukuokaK, KimuraY, et al.An ingenious deep learning approach for pressure injury depth evaluation with limited data. J Tissue Viability, 2024; 33(3):387–392; doi: 10.1016/j.jtv.2024.05.009
31.
KimJ, LeeC, ChoiS, et al.Augmented decision-making in wound care: Evaluating the clinical utility of a deep-learning model for pressure injury staging. Int J Med Inform, 2023; 180:105266; doi: 10.1016/j.ijmedinf.2023.105266
32.
KosmopoulosDI, TzevelekouFL. Automated pressure ulcer lesion diagnosis for telemedicine systems. IEEE Eng Med Biol Mag, 2007; 26(5):18–22; doi: 10.1109/emb.2007.901786
33.
LauCH, YuKHO, YipTF, et al.An artificial intelligence-enabled smartphone app for real-time pressure injury assessment. Front Med Technol, 2022; 4:905074; doi: 10.3389/fmedt.2022.905074
34.
LiuTJ, ChristianM, ChuYC, et al.A pressure ulcers assessment system for diagnosis and decision making using convolutional neural networks. J Formos Med Assoc, 2022; 121(11):2227–2236; doi: 10.1016/j.jfma.2022.04.010
35.
OrciuoliF, OrciuoliFJ, PedutoA. A mobile clinical DSS based on augmented reality and deep learning for the home cares of patients afflicted by bedsores. Procedia Comput Sci, 2020; 175:181–188; doi: 10.1016/j.procs.2020.07.028
36.
SakakibaraS, TakekawaA, TakekawaC, et al.Construction and validation of an image discrimination algorithm to discriminate necrosis from wounds in pressure ulcers. J Clin Med, 2023; 12(6):2194; doi: 10.3390/jcm12062194
37.
SeoS, KangJ, EomIH, et al.Visual classification of pressure injury stages for nurses: A deep learning model applying modern convolutional neural networks. J Adv Nurs, 2023; 79(8):3047–3056; doi: 10.1111/jan.15584
38.
ShiraishiM, KanayamaK, KuritaD, et al.Performance of artificial intelligence chatbots in interpreting clinical images of pressure injuries. Wound Repair Regen, 2024; 32(5):652–654; doi: 10.1111/wrr.13189
39.
SwerdlowM, GulerO, YaakovR, et al.Simultaneous Segmentation and Classification of Pressure Injury Image Data Using Mask-R-CNN. Comput Math Methods Med, 2023; 2023:3858997; doi: 10.1155/2023/3858997
40.
WangY, JiangXQ, YuKY, et al.Infrared thermal images classification for pressure injury prevention incorporating the convolutional neural networks. Ieee Access, 2021; 9:15181–15190; doi: 10.1109/access.2021.3051095
41.
WangD, GuoL, ZhongJ, et al.A novel deep-learning based weighted feature fusion architecture for precise classification of pressure injury. Front Physiol, 2024; 15:1304829; doi: 10.3389/fphys.2024.1304829
42.
YilmazB, AtagünE, DemırcanFÖ, et al.Classification of pressure ulcer images with logistic regression. IEEE International Symposium, 2021; pp. 1–6; doi: 10.1109/INISTA52262.2021.9548585
KandulaUR. Impact of multifaceted interventions on pressure injury prevention: A systematic review. BMC Nurs, 2025; 24(1):11; doi: 10.1186/s12912-024-02558-9
45.
KaradaǧA, ÇakarV, DemirAS. An inter-assessor reliability study on the categorization and staging of pressure injuries. J Tissue Viability, 2024; 33(4):786–791; doi: 10.1016/j.jtv.2024.09.009
46.
JiangXQ, WangY, WangYX, et al.Application of an infrared thermography-based model to detect pressure injuries: A prospective cohort study. Br J Dermatol, 2022; 187(4):571–579; doi: 10.1111/bjd.21665
47.
ToffahaKM, SimseklerMCE, OmarMA. Leveraging artificial intelligence and decision support systems in hospital-acquired pressure injuries prediction: A comprehensive review. Artif Intell Med, 2023; 141:102560; doi: 10.1016/j.artmed.2023.102560
48.
ZahiaS, ZapirainMBG, SevillanoX, et al.Pressure injury image analysis with machine learning techniques: A systematic review on previous and possible future methods. Artif Intell Med, 2020; 102:101742; doi: 10.1016/j.artmed.2019.101742
49.
GaoL, JiaoT, FengQ, et al.Application of artificial intelligence in diagnosis of osteoporosis using medical images: A systematic review and meta-analysis. Osteoporos Int, 2021; 32(7):1279–1286; doi: 10.1007/s00198-021-05887-6
50.
JiangM, MaY, GuoS, et al.Using machine learning technologies in pressure injury management: Systematic review. JMIR Med Inform, 2021; 9(3):e25704; doi: 10.2196/25704
51.
MesaH, VeredasFJ, MorenteL. A hybrid approach for tissue recognition on wound images. International Conference on Hybrid Intelligent Systems (HIS), 2008; pp. 120–125; doi: 10.1109/HIS.2008.33
52.
FulbrookP, LovegroveJ. Reporting accuracy of pressure injury categorisation in an acute tertiary hospital: A four-year analysis. J Clin Nurs, 2023; 32(17–18):6403–6414; doi: 10.1111/jocn.16662
53.
LanT, LiZ, ChenJ. FusionSegNet: Fusing global foot features and local wound features to diagnose diabetic foot. Comput Biol Med, 2023; 152:106456; doi: 10.1016/j.compbiomed.2022.106456
54.
ZaratkiewiczS, GoetcheusM, VanceH. Unstageable pressure injuries: Identification, treatment, and outcomes among critical care patients. Crit Care Nurs Clin North Am, 2020; 32(4):543–561; doi: 10.1016/j.cnc.2020.08.005
55.
KayserSA, VanGilderCA, AyelloEA, et al.Prevalence and analysis of medical device-related pressure injuries: Results from the International Pressure Ulcer Prevalence Survey. Adv Skin Wound Care, 2018; 31(6):276–285; doi: 10.1097/01.ASW.0000532475.11971.aa
56.
SaidiP, DasarathyG, BerishaV. Unraveling overoptimism and publication bias in ML-driven science. Patterns (N Y), 2025; 6(4):101185; doi: 10.1016/j.patter.2025.101185
57.
WuQ, GuoH, LiR, et al.Deep learning and machine learning in CT-based COPD diagnosis: Systematic review and meta-analysis. Int J Med Inform, 2025; 196:105812; doi: 10.1016/j.ijmedinf.2025.105812
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
