{"paper_id":"7debee29-14de-4fef-a24a-d0f30e7f8df0","body_text":"Research Article   \nSWOT Analysis of Vision Transformers for Automated \nDiagnosis of Endometriosis from Laparoscopic Videos: \nFeasibility and Ethical Challenges \nParnian Haqiqat 1 , Amir Amininejad 2* , Fardis Rouzbeh 3 , Kosar Gholami 4   \nHanieh Gholami 5  \n \nSubmitted: 14 Mar. 2025; Accepted: 28 Apr. 2025; Published: 01 May. 2025      \nIntroduction \nAutomated analysis of laparoscopic videos is emerging as a valuable tool for improving the detec-\ntion of diseases such as endometriosis, offering the potential to assist surgeons with complex in-\ntraoperative decisions and to standardize diagnostic accuracy [1, 2]. Traditional deep learning \napproaches for surgical video analysis have relied heavily on convolutional neural networks \n(CNNs) and recurrent neural networks (RNNs), which excel at capturing local spatial features and \n \n1. Department of Gynecology, Babol University of Medical Sciences, Babol, Iran; 2*. Corresponding author: Department \nof Surgery, Shahid Beheshti University Medical Sciences, Tehran, Iran. Email: amir.najafdar@gmail.com ; 3. Faculty of \nMedicine, Mazandaran University of Medical science, Mazandaran, Iran.4. Student Research Committee, Semnan Uni-\nversity of Medical Sciences, Semnan, Iran; 5. Student Research Committee, Babol University of Medical Sciences, Babol, \nIran. / Open Access. © 202 5 the author(s), published by InfoPub.  This work is licensed under the Creative Commons \nAttribution 4.0 International License. (Journal homepage: https://www.isjtrend.com) \nhttps://doi.org/10.61186/ist.202502.05.02  \nEndometriosis diagnosis via laparoscopy remains challenging due to subtle lesion appearances and in-\nter-observer variability. While artificial intelligence shows promise for surgical video analysis, the po-\ntential of Vision Transformers (ViTs) specifically for endometriosis detection remains unexplored. This \nstudy applied a SWOT framewor k to evaluate ViTs for automated endometriosis diagnosis in laparo-\nscopic videos. Analysis of 10 studies from PubMed, IEEE Xplore, and Scopus identified key findings: \nStrengths included (1) global attention for lesion detection, (2) outperforming CNNs/RNNs in surgical \ntasks (91-97% accuracy), and (3) multimodal data integration. Weaknesses were (1) dependence on \nunavailable annotated datasets, (2) high computational needs, (3) limited local feature sensitivity, and \n(4) annotation variability issues. Opportun ities involved (1) self -supervised learning from unlabeled \nvideos and (2) explainable attention maps. Threats comprised (1) performance variability across surgi-\ncal settings, (2) lacking regulatory standards, and (3) data privacy concerns. Crucially, no stu dies di-\nrectly tested ViTs for endometriosis diagnosis despite their potential. For clinical implementation, three \nrequirements emerged: (1) collaborative dataset creation, (2) optimized hybrid architectures, and (3) \nethical guidelines for surgical AI. This structured analysis provides a roadmap for developing ViT-based \ndiagnostic tools while addressing current limitations in data, technology, and clinical integration . \n \n Vision Transformers (ViTs), Endometriosis, Laparoscopic Surgery, SWOT Analysis, \nEthical AI.    \n\n \n \nInfoScience Trends ǁ (2025) NO 05; VOL 02: 11-22 ǁ ǁ https://doi.org/10.61186/ist.202502.05.02ǁ InfoPub \nwww.isjtrend.com \nshort-term temporal patterns but often struggle with the nuanced and diffuse presentations typ-\nical of gynecologic pathologies like endometriosis [3, 4]. Advances in Vision Transformers (ViTs) \nand transformer -inspired architectures have introduced a new paradigm capable of modeling \nglobal spatial and temporal relationships in video data, resulting in superior performance over \nconventional models for tool detection, workflow analysis, and related tasks within laparoscopy \n[2, 5]. \nDespite this progress, the application of ViT -based models to direct disease diagnosis from \nlaparoscopic video, and specifically to endometriosis identification, remains largely uncharted in \nthe published literature  [6, 7]. The feasibility of deploying such models is challenged by factors \nincluding data scarcity, high annotation costs, and significant computational demands required \nfor real-time clinical integration [4, 8]. Moreover, ethical issues such as annotation burden, data \nprivacy, interpretability, and regulatory acceptance have not been systematically explored in the \ncontext of transformer-based surgical video analysis [7, 9]. \nTo systematically assess these complex dimensions, the SWOT (Strengths, Weaknesses, Op-\nportunities, Threats) analysis framework is increasingly used in technology evaluation [10]. \nSWOT provides a structured approach to examining both the internal capabilities and limitations \nof a technology, as well as the external factors that may facilitate or hinder its clinical adoption \n[11]. By identifying strengths and weaknesses intrinsic to ViT architectures, and analyzing oppor-\ntunities and threats rooted in clinical, ethical, or regulatory contexts, a SWOT analysis can offer a \ncomprehensive perspective for guiding future research and practical implementation. \nThe purpose of this study is to bridge these critical gaps by providing a structured SWOT anal-\nysis of Vision Transformer models for the automated diagnosis of endometriosis from laparo-\nscopic videos. This review aims to evaluate the current state of technical feasibility and to high-\nlight the ethical challenges involved, offering practical considerations for future research and clin-\nical translation. \nMethods \nStudy Design  \nThis study employs a qualitative, structured SWOT (Strengths, Weaknesses, Opportunities, \nThreats) analysis to systematically evaluate the feasibility and ethical challenges of deploying Vi-\nsion Transformer (ViT) architectures for automated diagnosis of endometriosis from laparoscopic \nvideo data. \nLiterature Search and Data Sources \nA comprehensive literature search was conducted using PubMed, IEEE Xplore and Scopus to \nidentify primary research articles, systematic reviews, and technical reports relevant to: \n• Vision Transformer (ViT) or transformer-inspired models in medical image/video analy-\nsis \n• Automated disease diagnosis in laparoscopy, including but not limited to endometriosis \n• Feasibility, clinical integration, and ethical aspects of AI in surgical video analysis \nSearch terms included combinations of Technology Terms: \" vision transformer\" OR \"ViT\" OR \n\"visual transformer\" OR \"transformer architecture\" OR \"attention mechanism\". Clinical Application \nTerms: \"laparoscopic video\" OR \"surgical video\" OR \"endoscopic video\" OR \"minimally invasive sur-\ngery\". Diagnosis Terms: \"disease diagnosis\" OR \"pathology detection\" OR \"lesion identification\" OR \n\n \n \nInfoScience Trends ǁ (2025) NO 05; VOL 02: 11-22 ǁ ǁ https://doi.org/10.61186/ist.202502.05.02ǁ InfoPub \nwww.isjtrend.com \n\"medical diagnosis\"  .Analysis Framework Terms: \"technology assessment\" OR \"clinical implemen-\ntation\" OR \"adoption barriers\". Ethical Considerations:  \"ethical challenges\" OR \"AI ethics\" OR \"al-\ngorithmic bias\", and related synonyms. \nInclusion and Exclusion Criteria \nInclusion criteria \n• Peer-reviewed articles or preprints published in English \n• Studies describing ViT or transformer -based machine learning approaches in laparo-\nscopic video analysis \n• Papers addressing automated disease diagnosis, particularly for endometriosis, or dis-\ncussing general feasibility and ethical considerations for AI in surgical video \nExclusion criteria \n• Studies focusing solely on surgical phase recognition, tool detection, or workflow analysis \nwithout disease/pathology diagnosis \n• Articles without explicit discussion of model strengths, weaknesses, opportunities, or \nthreats \nSWOT Framework Development \nDrawing on the included literature and expert domain knowledge, a SWOT matrix was con-\nstructed to capture: \n• Strengths: Inherent capabilities and advantages of ViT models for disease detection in \nlaparoscopic video \n• Weaknesses: Technical and practical limitations, including data and clinical constraints \n• Opportunities: External factors and future directions that may facilitate clinical adop-\ntion, innovation, or improved outcomes \n• Threats: Risks, barriers, and ethical concerns associated with real -world deployment \nand broader societal implications \nEvaluation Process \nEach article was independently reviewed by two researchers. Factual statements regarding \nViT architectures, feasibility, and ethical issues were extracted and categorized into the SWOT \ncomponents. Discrepancies were resolved through discussion. \nResults \nSystematic Literature Search and Screening Process \nOur comprehensive search across PubMed (n=45), IEEE Xplore (n=32), and Scopus (n=28) \ninitially identified 105 articles published between 2018 –2023. After removing 23 duplicates, 82 \nunique records underwent title/abstract screening. Of these, 52 were excluded for irrelevance to \nlaparoscopic video analysis or lack of focus on Vision Transformers (ViTs). \nThe remaining 30 full-text articles were assessed against predefined inclusion/exclusion cri-\nteria. 20 additional studies were excluded for the following reasons: \n• 12 studies focused exclusively on surgical tool detection or workflow segmentation \nwithout disease diagnosis. \n• 7 studies employed CNNs/RNNs only, omitting ViT or transformer-based architectures. \n• 1 study was excluded due to the non-English language. \n\n \n \nInfoScience Trends ǁ (2025) NO 05; VOL 02: 11-22 ǁ ǁ https://doi.org/10.61186/ist.202502.05.02ǁ InfoPub \nwww.isjtrend.com \nThe final 10 articles (Table 1) met all criteria, including: \n• Use of ViT or hybrid transformer models in laparoscopic/endoscopic video analysis. \n• Relevance to automated disease diagnosis (though none addressed endometriosis \ndirectly). \n• Discussion of feasibility, clinical integration, or ethical challenges. \nNotably, PubMed contributed 6 studies, IEEE Xplore 3, and Scopus 1, reflecting the interdis-\nciplinary nature of the field (computer science vs. clinical journals). \nTable 1. Summary of Included Studies on Vision Transformers (ViTs) in Surgical Video Analysis. \nStudy Purpose Type Method & \nMaterials Results Conclusion \n[9] \nDevelop and assess a \nmultimodal \ntransformer model for \nanalyzing \nlaparoscopic surgical \nvideos, aiming to \nreduce negative \noutcomes and \nimprove patient \nsafety. \nOriginal \nresearch \n(empirical) \nMultimodal model \ninspired by Video-\nAudio-Text \nTransformer; uses \nViT for images and \nBERT for text; \ntrained on \nCholec80 LC \nvideos with \nvarious \ncomplexities. \nMean accuracy \n91.0%, precision \n81%, recall 83% on \n30/80 test videos \n(Cholec80 dataset). \nShows the model \ncan extract hidden \nand distinct \nfeatures, helping to \ncreate safer surgery \nsystems, but the \noverall role and \nadvantages of AI \nmodels in surgery \nremain uncertain. \n[12] \nPropose dataset and \nmethod for event \nrecognition in \nlaparoscopic \ngynecology videos; \nevaluate hybrid \ntransformer for \ndetecting critical \nintra- and post-\noperative events. \nOriginal \nresearch \n(empirical) \nIntroduces \nannotated event \ndataset; compares \nseveral CNN-RNN \nmodels; develops \nhybrid \ntransformer \narchitecture for \nevent recognition; \nuses frame \nsampling. \nThe hybrid \ntransformer \nimproves recognition \naccuracy, counteracts \nocclusion/motion \nblur, and yields high \ntemporal resolution \nin event recognition. \nThe proposed \nhybrid transformer \napproach is \nsuperior to existing \nCNN-RNN methods \nfor event \nrecognition in \nlaparoscopic \nsurgery videos. \n[2] \nDetect surgical tool \npresence in \nlaparoscopic video \nusing a transformer \narchitecture \n(LapFormer). \nOriginal \nresearch \n(empirical) \nLapFormer model: \nfeed-forward \ntransformer with \nattention for inter-\nframe correlation; \nevaluated on \nCholec80 dataset. \nOutperforms CNN \nand RNN baselines by \n20.3 and 17.3 points, \nrespectively, in \nmacro-F1 score; \nincludes ablation \nstudies. \nTransformer \narchitecture is \nmore effective than \nprevious methods \nfor surgical tool \ndetection in \nlaparoscopic \nvideos. \n[7] \nExplore use of pure \nvision transformers \nfor classifying single- \nand multi-label \nsurgical tool frames in \nlaparoscopic surgery. \nOriginal \nresearch \n(empirical) \nPure ViT models \nfor SL/ML tool \nclassification; 5-\nfold cross-\nvalidation on \nCholec80 dataset. \nMean average \nprecision (mAP) = \n95.8%, \noutperforming \nconventional multi-\nlabel models. \nResults suggest \npromise for ViT \nmodels in surgical \ntool detection, \nwarranting further \nresearch. \n[13] \nPresent a \nbidirectional \ntransformer with \nsparse attention for GI \ndisease recognition \nfrom endoscopy \nimages. \nOriginal \nresearch \n(empirical) \nBidirectional \nTransformer with \nSparse Attention \n(BTSA); trained \nand tested on \nlarge-scale GI \nendoscopy \ndatasets. \nBTSA achieves \noutstanding \nperformance, \nsurpasses existing \nmodels in GI disease \nrecognition, and is \nefficient. \nModel shows \nsignificant \npotential, but \nfurther research \nand validation are \nneeded for clinical \nutility. \n[8] \nBenchmark deep \nlearning models \n(convolutional, \ntransformer, hybrid) \nfor surgical \nworkflow/phase \nOriginal \nresearch \n(empirical) \nCompares fully \nconvolutional, fully \ntransformer, and \nhybrid models; \nworkflow \nrecognition from \nHybrid model \nachieves 93% frame-\nlevel accuracy and 85 \nsegmental edit \ndistance; fully \ntransformer also gets \nHybrid models \neffectively capture \nworkflow and yield \nbest results in \nsurgical video \nanalysis. \n\n \n \nInfoScience Trends ǁ (2025) NO 05; VOL 02: 11-22 ǁ ǁ https://doi.org/10.61186/ist.202502.05.02ǁ InfoPub \nwww.isjtrend.com \nrecognition in \nendoscopic videos. \ngastric bypass \nsurgery videos. \nstate-of-the-art \nresults. \n[14] \nDevelop a CNN with a \nnew attention module \n(P-CSEM) to improve \nlaparoscopic tool \ndetection. \nOriginal \nresearch \n(empirical) \nCNN with P-CSEM \nattention modules \nat various layers; \ntrained and tested \non Cholec80 \ndataset. \nAttention-enhanced \nmodel achieves mean \naverage precision of \n93.14%; feature \nrelevance improved. \nCustom attention \nmodules inside \nCNNs can enhance \ntool relevance in \nlaparoscopic tool \nclassification. \n[15] \nUse a transformer-\nbased, multi-view \napproach to improve \nsurgical phase \nrecognition in \nlaparoscopic \ncholecystectomy. \nOriginal \nresearch \n(empirical) \nMulti-view phase \nrecognition using \ntransformer-based \nmodel with late \nfusion of \nlaparoscopic and \nin-room camera \ndata. \nPerformance is \nmixed; real-world \ndata collection \nintroduced \nchallenges; model \nperformance \ndecreased with poor \ndata. \nIntegration of \nmulti-view data is \ncomplex; real-\nworld diversity and \nbetter data are \nneeded for optimal \nmodels. \n[16] \nReview and \nsummarize deep \nlearning methods \n(incl. transformers) \nfor phase and step \nrecognition in surgical \nworkflow analysis. \nSystematic \nreview \n(review \narticle) \nSystematic review; \nsearched \ndatabases for \nstudies post-2018 \non deep learning \nmethods for \nsurgical workflow; \n44 studies \nreviewed. \nTemporal context \n(RNN, CNN, \nTransformers) is key; \nlack of diverse \ndatasets is a major \nchallenge in \nworkflow \nrecognition. \nThe field is \nadvancing, but \nrobust, \ngeneralizable \nmodels are \nhampered by \nlimited, supervised \ndatasets. \n[4] \nCompare performance \nof ViT, CNN, and \nhybrid CNN-ViT \narchitectures for GI \ndisease classification \nfrom endoscopy \nimages. \nOriginal \nresearch \n(empirical) \nViT, hybrid CNN-\nViT, CNN \narchitectures; \ntrained on GI \nendoscopy images \n(WCE, etc.); \nclassified 6 GI \ndisease classes. \nHybrid model \nachieves test \naccuracy of 97.91%, \nF1 97.91%, precision \n98.01%; compared \nagainst CNN and ViT \nmodels. \nHybrid CNN-ViT \nmodels can \naccurately classify \nGI diseases from \nendoscopy images. \nVision Transformer (ViT) or Transformer-Inspired Models in Medical Image/Video Analysis \nRecent years have seen increased application of Vision Transformer (ViT) and transformer -\ninspired architectures in medical image and video analysis. Multiple studies have focused on lev-\neraging the global attention mechanisms of ViTs and their hybrid forms for diverse tasks, includ-\ning tool detection, workflow and phase recognition, and disease diagnosis in gastrointestinal en-\ndoscopy images. These models have shown performance improvements over traditional CNNs or \nRNNs, especially for spatio-temporal data in laparoscopic and endoscopic domains. However, the \npredominant use cases reported in the literature relate to surgical tool or workflow recognition, \nwith relatively few studies targeting disease diagnosis, and none specifically focusing on endome-\ntriosis in laparoscopic videos [4, 7-9, 12] (Table 2). \nTable 2. Applications of ViTs in Laparoscopic/Endoscopic Video Tasks. \nStudy Task Data Type ViT Usage Key Outcome \n[3] Surgical tool detection Laparoscopic video Pure ViT Superior macro-F1 to CNN/RNN \n(+20%) \n[7] \nTool classification \n(SL/ML) Laparoscopic video Pure ViT mAP 95.8%; outperforming \nprevious CNN models \n[8] \nWorkflow/phase \nrecognition Laparoscopic video ViT/CNN/Hybrid Transformers match SOTA \nconvolutional results \n[4] GI disease classification Capsule endoscopy \nimg ViT/Hybrid CNN ViT and hybrids outperform \nCNN only \n[13] GI disease recognition GI endoscopy \nimages Bi-directional T Sparse attention improves \nefficiency \n \n \n\n \n \nInfoScience Trends ǁ (2025) NO 05; VOL 02: 11-22 ǁ ǁ https://doi.org/10.61186/ist.202502.05.02ǁ InfoPub \nwww.isjtrend.com \nAutomated Disease Diagnosis in Laparoscopy, Including but Not Limited to Endometriosis \nAutomated disease diagnosis in laparoscopy remains a relatively underexplored application \ncompared to tool detection or workflow recognition. Across the reviewed literature, there are no \nstudies directly addressing endometriosis detection with ViT -based methods from laparoscopic \nvideos. While several references evaluate transformers for disease classification in gastrointesti-\nnal endoscopy images, these studies do not utilize laparoscopic video data nor focus on gyneco-\nlogical pathologies such as endometriosi s. The only disease diagnosis applications identified re-\nlate to GI pathologies from endoscopy, not laparoscopy [4]. Tool and event recognition studies in \nlaparoscopy do not provide systematic assessment of disease classification performance [7-9, 12] \n(Table 3). \nTable 3. Gap Analysis: Disease Diagnosis in Laparoscopy vs. Endoscopy. \nStudy Disease Task Method Laparoscopic \nVideo Outcome/Limitations \n[13] \nGI disease \n(general) Bi-directional T No Applies to endoscopy, not laparoscopy \n[4] \nGI disease (6 \ntypes) ViT/Hybrid CNN No Only WCE images, not laparoscopy \nvideo \n[2, 7-9, \n12] Tool/phase/event ViT/Hybrid/CNN Yes No disease/pathology classification \ntask \nFeasibility, Clinical Integration, and Ethical Aspects of AI in Surgical Video Analysis \nFeasibility and clinical integration of AI models—especially Vision Transformers—in surgical \nvideo analysis are discussed peripherally in several empirical studies, though not as primary out-\ncomes. The main technical feasibility concerns center on data scar city, need for large and granu-\nlarly annotated training datasets, and high computational demands for real-time application. For \nclinical integration, general requirements include high interpretability (attention -based \nheatmaps), robust performance across ha rdware/sites, and alignment with surgical workflow. \nEthical aspects, such as annotation burden, trust in AI, and concerns over robust deployment, are \nnoted in generic terms in the literature, with no detailed frameworks specific to ViT-based disease \ndiagnosis in laparoscopy [4, 7-9]. Safety, explainability, and regulatory barriers are highlighted as \nongoing challenges (Table 4).  \nTable 4. Key Feasibility and Ethical Challenges for ViT Deployment. \nAspect Noted Issues Studies Citing the Issue \nData hunger Requires large annotated datasets [2, 4, 7-9] \nComputation Resource-intensive; real-time limits [2, 7-9] \nInterpretation Needs explainable attention maps [2, 9] \nAnnotation cost High labeling burden [2, 7, 8] \nEthical/safety Clinical trust, regulatory hurdles [2, 4, 7, 8] \nSWOT Analysis \nStrengths of ViT for Automated Diagnosis of Endometriosis from Laparoscopic Videos  \nVision Transformers provide a global receptive field allowing for comprehensive aggregation \nof visual information across each frame and, in extended forms, across video sequences. This spa-\ntial (and potentially temporal) attention facilitates detection of diffuse, subtle, or irregularly \nshaped disease lesions commonly seen in endometriosis. ViTs are also highly adaptable to multi-\nmodal fusion, supporting the integration of additional context such as surgical reports or instru-\nment data. Empirical studies in tool and workflow recognition indicate ViTs can outperform tra-\nditional CNNs, particularly when applied to well-annotated surgical video datasets [2, 7-9]. \n\n \n \nInfoScience Trends ǁ (2025) NO 05; VOL 02: 11-22 ǁ ǁ https://doi.org/10.61186/ist.202502.05.02ǁ InfoPub \nwww.isjtrend.com \nWeaknesses of ViT for Automated Diagnosis of Endometriosis from Laparoscopic Videos  \nA primary weakness is the requirement for large quantities of diverse, accurately annotated \nvideo data for effective model training—resources scarce in laparoscopic disease diagnosis, espe-\ncially for conditions like endometriosis. ViTs are computationally d emanding, posing challenges \nfor real-time operating room integration without significant hardware support or model optimi-\nzation. Their limited inherent inductive bias for local features can reduce sensitivity to small or \nlocalized lesions, unless modified with hybrid architectures. Issues with label quality and interob-\nserver variability in disease annotation may also affect reliability [2, 7-9]. \nOpportunities of ViT for Automated Diagnosis of Endometriosis from Laparoscopic Videos  \nEmerging opportunities include leveraging self-supervised pretraining on large, unannotated \nlaparoscopic video archives to reduce manual labeling requirements and improve model robust-\nness. Federated learning across multiple centers can help build generaliz able models while pre-\nserving patient privacy. Explainable AI techniques leveraging visual attention maps may facilitate \nclinical acceptance by enhancing model interpretability. Additionally, advances in spatio -tem-\nporal transformer variants and multimodal a pproaches could further enhance disease detection \ncapabilities [2, 4, 7-9]. \nThreats of ViT for Automated Diagnosis of Endometriosis from Laparoscopic Videos  \nKey threats include vulnerability to domain shifts caused by different surgical equipment, \ncamera systems, lighting conditions, and operator techniques —all of which could degrade ViT \nmodel performance if not addressed. The lack of standardized datasets and  high-quality labeled \nexamples for endometriosis remains a significant barrier. Further, regulatory approval for clinical \ndeployment may be hindered by the complexity and opacity of transformer models, challenges \nwith real-time guarantees, and persistent c oncerns about trust and accountability in automated \ndecision-making [2, 4, 7, 8]. \nSWOT Matrix for ViT-Based Endometriosis Diagnosis from Laparoscopic Videos \nThe SWOT matrix synthesizes internal (Strengths/Weaknesses) and external (Opportuni-\nties/Threats) factors influencing ViT -based endometriosis diagnosis. Strengths highlight ViTs’ \nglobal attention for detecting diffuse lesions, while weaknesses address data/computational de-\nmands. Opportunities include federated learning and explainable AI, whereas threats encompass \ndomain shifts and  regulatory barriers. This framework guides balanced evaluation for clinical \nadoption (Figure 1). \n\n \n \nInfoScience Trends ǁ (2025) NO 05; VOL 02: 11-22 ǁ ǁ https://doi.org/10.61186/ist.202502.05.02ǁ InfoPub \nwww.isjtrend.com \n \nFigure 1. SWOT Matrix for ViT-Based Endometriosis Diagnosis from Laparoscopic Videos. \nFeasibility and Ethical Challenges \nThe use of Vision Transformer (ViT) architectures for automated disease diagnosis in laparo-\nscopic videos offers significant promise but also faces considerable challenges (Table 5). Moreo-\nver, integrating ViT-based diagnostic models into clinical laparoscopic workflows introduces sev-\neral ethical concerns, many of which remain unresolved in the literature. A key challenge is the \nannotation burden: developing large, high -quality labeled datasets for surgical videos demands \nsubstantial time from medical experts and raises potential risks regarding patient data privacy [2, \n8] (Table 5). \nTable 5. Feasibility and Ethical Challenges for ViT-based Automated Diagnosis of Endometriosis from Laparoscopic \nVideos. \nDomain Challenges/Considerations References \nFeasibility \n- Large, annotated laparoscopic video datasets are scarce, especially with \ndisease/pathology labels (e.g., endometriosis). [2, 4, 7, 8] \n- ViTs require substantial computational resources; real-time clinical OR \ndeployment is challenging. [7, 8] \n- Annotation requires expert clinicians, leading to high costs and annotation \nburden. [2, 7, 8] \n- Self-supervised pretraining and federated learning proposed to address data \nscarcity and improve generalization but are not yet standard in this context. [4, 9] \n- Hybrid or sparse attention architectures may partially alleviate computational \nloads; further validation is needed. [8, 13] \n- No direct empirical evidence exists for ViT feasibility in endometriosis \ndiagnosis; existing evidence is from adjacent tasks (tool/phase detection). [2, 4, 7, 9] \nEthical - High annotation burden and privacy concerns associated with creation and use \nof surgical video datasets. [2, 7, 8] \nChallenges \n- Potential for bias (class imbalance, label noise, interobserver variability) may \ncompromise fairness and reliability. [4, 8, 16] \n- \"Black-box\" nature of ViTs limits interpretability; attention maps help but have \nlimits. [2, 9] \n- Regulatory, accountability, and liability issues are more complex due to ViT \nmodel opacity and performance uncertainty. [7, 8] \n- Consent, secure data storage, and transparency are unresolved in current \nframeworks for AI in surgical video analysis. [2, 4, 7, 8] \n\n\n \n \nInfoScience Trends ǁ (2025) NO 05; VOL 02: 11-22 ǁ ǁ https://doi.org/10.61186/ist.202502.05.02ǁ InfoPub \nwww.isjtrend.com \nImplications \nThe absence of direct evidence for ViT -based endometriosis diagnosis underscores a critical \ngap in both the research landscape and clinical translation efforts for advanced AI models in gy-\nnecologic laparoscopy. While the strengths of transformers —such as global attention, flexibility \nfor integrating multiple data modalities, and state-of-the-art performance—present clear oppor-\ntunities for improving disease detection, significant weaknesses and external threats must be ad-\ndressed. These include the need for large -scale, high-quality annotated datasets, computational \nbarriers to real-time deployment, vulnerability to domain shift, and lack of explainability required \nfor surgeon trust and regulatory approval. The practical integration of ViT-based diagnostic AI in \nsurgical settings therefore depends not only on further technological innovation, but also on ro-\nbust clinical validation, ethical guidelines, and cross-disciplinary collaboration. \nRecommendations for Practice \n1. Data Infrastructure and Sharing Collaborative efforts should be initiated to develop, \nstandardize, and share large, annotated laparoscopic video datasets—specifically \nincluding cases of endometriosis—to enable model training and robust benchmarking [2, \n7, 8]. \n2. Model Development and Validation Researchers should explore hybrid and spatio-\ntemporal transformer architectures, leveraging approaches such as self-supervised and \nfederated learning to address data scarcity and improve cross-site generalizability [2, 4, \n7-9]. \n3. Interpretability and Trust Development of explainable AI techniques—such as attention \nheatmaps—should be prioritized to support clinical interpretation and facilitate trust \namong surgeons and regulatory bodies [2, 9]. \n4. Ethical and Regulatory Oversight Early integration of ethical considerations, including \nannotation burden, bias, and informed consent, is essential. Engagement with regulatory \nagencies should guide the development of compliance-ready AI systems [2, 4, 7, 8]. \n5. Clinical Integration Pilot implementation of ViT-based diagnostic support tools should be \nclosely monitored in controlled settings, ensuring that workflow integration, \ncomputational requirements, and real-time performance meet clinical and patient safety \nstandards [8]. \nDiscussion \nThis study provides the first structured SWOT analysis of Vision Transformer (ViT) models for \nautomated diagnosis of endometriosis from laparoscopic videos —a task that remains un-\naddressed directly in the current literature. While recent advances in deep learning have estab-\nlished ViT and transformer -inspired architectures as state -of-the-art tools for complex visual \ntasks, a comparative examination of their performance, limitations, and broader implications in \nsurgical video analysis is essential for informed development and adoption. \nCompared to prior convolutional and recurrent approaches, ViT models show clear empirical \nstrengths in related laparoscopic video analysis tasks. For instance, in tool detection and work-\nflow/phase recognition, ViT -based and hybrid transformer models achiev ed superior accuracy \nand macro-F1 scores over conventional CNN and RNN baselines [2, 7, 8]. These gains stem largely \nfrom the global attention mechanisms that allow ViTs to model spatial dependencies and long -\nrange context more efficiently than convolutional filters or sequential RNN layers. Additionally, \nViT architectures are highly adaptable: studies show their applicability to single- and multi-label \n\n \n \nInfoScience Trends ǁ (2025) NO 05; VOL 02: 11-22 ǁ ǁ https://doi.org/10.61186/ist.202502.05.02ǁ InfoPub \nwww.isjtrend.com \nclassification [7], as well as their extension to multimodal fusion, incorporating text and audio \ndata for richer surgical scene understanding [9]. \nHowever, in direct comparison, the domain of automated disease diagnosis—particularly for \nconditions like endometriosis—is far less developed for ViT -based models than the adjacent do-\nmains of surgical tool recognition or workflow segmentation. While transf ormer models have \nbeen applied with promising results to GI disease diagnosis from endoscopic images  [4], these \nstudies do not use laparoscopic video data, nor do they address gynecological pathologies specif-\nically. Tool detection and workflow studies in laparoscopy  [2, 7-9, 14, 15] consistently exclude \ndisease classification as an explicit outcome. This gap is striking, as it suggests that the advantages \nobserved for ViT models in instrument and phase tasks may not translate automatically to disease \nrecognition in the more heterogeneous and visually subtle context of endometriosis. \nFeasibility constraints form a recurrent theme across the literature. Data scarcity and annota-\ntion costs are reported as the primary bottlenecks for transformer training in laparoscopy [2, 7]. \nViT models require large datasets —often orders of magnitude greater than those needed for \nCNNs—to generalize well and avoid overfitting. This demand is particularly challenging in endo-\nmetriosis research, where annotated laparoscopic video collections are  uncommon. Computa-\ntional barriers also persist, with studies highlighting the significant memory and processing re-\nquirements of self-attention mechanisms, especially for long surgical videos [4,6]. In comparative \nterms, these challenges exceed those faced by most CNN/RNN -based pipelines and necessitate \ninnovations such as hybrid architectures, sparse attention mechanisms, or pretraining with large-\nscale unlabeled data [4, 9, 13]. \nEthically, the transformer literature underlines issues that are not unique to ViT models but \nare amplified by their complexity. Class imbalance, label noise, and interobserver variability —\nubiquitous in medical annotation—pose risks for fairness and reliability of automated disease di-\nagnosis [4, 8]. Where prior CNN/RNN approaches have already drawn scrutiny for their \"black -\nbox\" characteristics, ViTs—while capable of some explainability through attention maps [2, 9]—\nstill struggle to provide fully interpretable and trustworthy outputs. This complicates regulatory \napproval, clinical acceptance, and potential liability discussions. Furthermore, data-sharing inno-\nvations such as federated learning are only nascent and un tested in the context of disease detec-\ntion in laparoscopic video [4, 9]. \nComparing ViT adoption in laparoscopic disease diagnosis to its success in endoscopic GI dis-\nease tasks [4, 16], it becomes clear that pathologies with strong, localized visual signatures (e.g., \ncertain GI lesions) align more easily with patch-based transformer reasoning. For heterogeneous, \npoorly demarcated diseases such as endometriosis in laparoscopy, future res earch will need to \nconsider domain-adapted attention mechanisms or hybrid models that reintroduce local struc-\ntural biases [2, 8]. Moreover, workflow and tool recognition studies demonstrate the potential of \ntraining on larger, multi-institutional datasets [2, 8], a lesson directly transferable to the endome-\ntriosis context if privacy and labeling barriers can be overcome. \nIn summary, while there is strong comparative evidence that ViT models can achieve and \nsometimes exceed the performance of existing deep learning approaches in laparoscopy for tasks \nsuch as tool and workflow recognition  [7, 8], there remains a marked lack of direct research on \ntheir application to automated disease diagnosis—and none at all on endometriosis detection in \nlaparoscopic videos. The feasibility and ethical challenges identified here surpass those of previ-\nous generations of AI models and must be systematically addressed before ViT-based diagnostics \ncan be safely and successfully integrated into surgical practice. \n \n\n \n \nInfoScience Trends ǁ (2025) NO 05; VOL 02: 11-22 ǁ ǁ https://doi.org/10.61186/ist.202502.05.02ǁ InfoPub \nwww.isjtrend.com \n \nLimitations \nThis study has several limitations. First, the literature search was restricted to English -lan-\nguage articles, potentially omitting relevant non-English research. Second, the lack of direct stud-\nies on ViTs for endometriosis diagnosis necessitated extrapolation from adjacent tasks (e.g., tool \ndetection), which may not fully represent disease -specific challenges. Third, the SWOT analysis, \nwhile systematic, remains qualitative and could benefit from quantitative validation through \nstakeholder surveys or multice ntric data. Finally, rapid advancements in transformer architec-\ntures may outdate some technical feasibility assessments. \nConclusion \nVision Transformers (ViTs) demonstrate potential for automating endometriosis diagnosis in lap-\naroscopic videos, leveraging their ability to model global spatial relationships. However, this ap-\nplication remains understudied compared to tool or workflow recognition. Key barriers include \ndata scarcity, computational costs, and unresolved ethical concerns. Future work must prioritize \ncurated datasets, hybrid architectures for efficiency, and rigorous clinical validation to bridge the \ngap between technical promise and real-world utility. \nAbbreviations \nViT: Vision Transformer; CNN: Convolutional Neural Network; RNN: Recurrent Neural Network; \nSWOT: Strengths, Weaknesses, Opportunities, Threats; GI: Gastrointestinal. \nEthical approval  \nThis study analyzed published literature and did not involve human participants, primary data \ncollection, or patient interactions. As a text -based review, it required no institutional ethics ap-\nproval or compliance with declarations such as the Helsinki Code. \nAvailability of data and materials \nPlease contact the corresponding author if you would like access to the datasets used and/or \nanalyzed during this study. \nFunding \nThis research was not funded or supported by any organizations.  \nAuthors’ Contribution  \nP.H.: Conceptualization, literature review, manuscript drafting. AA.: Data curation, table/figure \ndesign, ethical analysis. F.R.: Methodology, literature screening, SWOT framework development. \nK.GH.: Abstract/keywords, limitations/conclusion drafting. H.GH.: References formatting, \ntechnical validation, final editing. \nAcknowledgment  \nNot applicable. \nConsent for publication  \nThe authors provided their consent for the publication of the study results. \nCompeting interests \nThe authors declare no competing interests. \n\n \n \nInfoScience Trends ǁ (2025) NO 05; VOL 02: 11-22 ǁ ǁ https://doi.org/10.61186/ist.202502.05.02ǁ InfoPub \nwww.isjtrend.com \nReferences  \n[1]. Maheux-Lacroix S, Belanger M, Pinard L, Lemyre M, Laberge P, Boutin A. Diagnostic Accuracy of Intraoperative \nTools for Detecting Endometriosis: A Systematic Review and Meta-analysis. Journal of minimally invasive \ngynecology. 2020;27(2):433-40.e1. https://doi.org/10.1016/j.jmig.2019.11.010  \n[2]. Kondo S. 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Demir KC, Schieber H, Weise T, Roth D, May M, Maier A, et al. Deep learning in surgical workflow analysis: a \nreview of phase and step recognition. IEEE Journal of Biomedical and Health Informatics. 2023;27(11):5405-\n17. https://doi.org/10.1109/jbhi.2023.3311628  \nPublisher’s Note \n \n© 2025 The Author(s). Published by InfoPub. \nPublisher homepage: https://infopub.ir/ \nDisclaimer: The views, opinions, and data presented in this article are solely those of the author(s) \nand do not necessarily reflect the official policy or position of InfoScience Trends or its editorial team. \nInfoScience Trends and its editors disclaim any liability for errors, consequences, or damages arising \nfrom the use of information contained in this publication.","source_license":"CC0","license_restricted":false}