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Evidence profile

Sourced from the future work and limitations of the source papers, classified as general, spanning 6 journals. Those papers have been cited 2 times in total.

Supporting evidence — 8 representative gaps

• Artificial intelligence assisted simulation and surgical video analytics for ophthalmic surgery training and competence development (2026) · Frontiers in Medicine · cited 2× · doi

  Artificial intelligence is progressively reshaping ophthalmic surgi- cal training from experience-dependent apprenticeship toward objec- tive, data-driven competence development. Across the novice to expert continuum, current evidence supports the role of AI-enabled simulation, computer-visio, and registry-driven predictive modeling as assistive tools that enhance feedback consistency, accelerate skill acquisition, and enable scalable benchmarking. Importantly, these systems function most effectively when aligned with clearly defined educational objectives and human oversight, rather than as autono- mous decision-makers. design, where safety risks are minimal. However, translation into real-world assistance will require stringent safeguards, transparent reward structures, and clear boundaries that preserve surgeon authority. 10.4 Federated learning and cross-center collaboration Sustainable progress in AI-assisted surgical education will depend on access to diverse, high-quality data. Federated learning provides a viable pathway to train robust models across institutions while pre- serving patient privacy and data sovereignty. For ophthalmic surgery, federated infrastructures could reduce center-specific bias, improve generalizability across devices and populations, and facilitate interna- tional benchmarking of training outcomes. More broadly, recent pre- dictive modeling studies in other ophthalmic subspecialties have likewise highlighted the importance of heterogeneous datasets and external validation, reinforcing the need for cross-center collaboration before AI models can be adopted as dependable decision-support tools (53). Looking forward, the next phase of AI evolution in ophthalmic surgery will extend beyond task-specific models toward integrated, multimodal intelligence frameworks that operate across the full surgi- cal lifecycle. Beyond the scope of this review, AI-integrated robotic assistance and closed-loop control for ophthalmic microsurgery represent an important parallel track that will require dedicated evidence synthesis and prospective clinical validation. 10.1 Multimodal foundation models for surgical understanding Future AI systems are expected to transition from single- modality video analysis to multimodal foundation models that jointly learn from surgical video, instrument kinematics, force- feedback signals, intraoperative OCT, and perioperative clinical data. For training, this may enable richer competency assessment that integrates technical precision, temporal efficiency, and cogni- tive load. However, robust external validation and standardized data interfaces will be essential before these models can be safely deployed at scale. 10.2 Digital twins of ocular anatomy and personalized simulation Another promising direction is the development of digital twins of ocular anatomy, constructed from patient-specific imaging and continuously updated surgical data. Such virtual replicas could allow surgeons to rehearse procedures under realistic biomechanical condi- tions, explore alternative strategies, and anticipate complication path- ways before entering the operating room. For trainees, digital twins may bridge the gap between generic simulation and individualized anatomy, accelerating the transition from rule-based execution to situ- ational reasoning. In summary, AI-assisted ophthalmic surgical training is entering a transition from isolated tools toward interconnected intelligence systems. The greatest impact will likely arise not from full automation, but from thoughtfully designed human–AI collaboration that aug- ments perception, supports reflection, and reinforces clinical respon- sibility. Future research should prioritize prospective, multi-center studies that link AI-enabled training interventions to long-term patient outcomes, thereby ensuring that technological advancement translates into meaningful clinical benefit.

  generalfuture workevidence 5/5
  Keywords: ophthalmic models training surgical across center clinical intelligence toward simulation tools systems federated collaboration patient
• Artificial intelligence and robotic technologies redefining precision and personalization in orthopedic surgery: a narrative review (2026) · Frontiers in Bioengineering and Biotechnology · doi

  AI algorithms form a robust technical foundation for intelligent assistance in orthopedic surgery, yet their translation into clinical practice remains constrained by key technical limitations. Foremost among these is the “black-box” nature of many AI models, which compromises interpretability and, consequently, adoption and clinical trust (Blackman and Veerapen, 2025; Zheng et al., 2025). In orthopedic surgical settings, doctors need to understand the reasoning behind AI-derived predictions to assess the validity of proposed operative pathways or risk assessments. Yet the opacity of algorithmic decision-making, coupled with the inherent complexity of underlying computational processes, often limits a surgeon’s ability to evaluate the logic of outputs, thereby reducing confidence and hindering real-world application (Kumar et al., 2022; Ramkumar et al., 2022; Pai et al., 2024). In recent years, advances in explainable AI have begun to address this challenge. For example, heatmap-based visualization tools can illustrate the specific anatomical regions or features influencing model outputs, enabling surgeons to better interpret AI recommendations and fostering greater trust in their application (Akula et al., 2022; Mukhtorov et al., 2023). A central objective of digital orthopedics is to optimize resource utilization; however, current algorithmic frameworks often demand substantial human input for design, validation, and refinement. For instance, in one study aimed at developing an AI system capable of automatically detecting metacarpal fracture lines, the initial model failed to achieve satisfactory accuracy. Consequently, 10 specialist physicians engaged in a 3-month training process to iteratively teach the system, enabling it to acquire basic automated recognition capabilities (Kim et al., 2024). Beyond algorithms, the hardware platforms that enable AI applications, such as intelligent navigation platforms, surgical robots, and sensor-based devices, are critical in translating computational outputs into precise clinical actions. Their performance and reliability directly determine clinical value, yet several barriers remain. A major challenge lies in the absence of standardized interfaces for heterogeneous data sources, which constrains the efficiency of multimodal data integration. In formats between orthopedic procedures, variations electronic health systems, and biomechanical sensors compel developers to create bespoke dataconversion modules for each device, increasing system complexity, prolonging development cycles, and introducing risks of data transmission errors (Su and Pei, 2024). in data imaging (EHRs), records for soft their algorithms compensation Technical limitations in intraoperative adaptability further impede precision. While certain orthopedic robotic systems employ generative AI to construct three-dimensional skeletal models, tissue deformation still rely primarily on static preoperative data and lack the capacity to fully adapt to dynamic intraoperative load variations, leading to suboptimal feedback during procedures (Ryu et al., 2024; Zhang Z. et al., 2024). Furthermore, device incompatibility and limited system-level interoperability impede technological communication protocols and operating systems among devices hinder real-time data synchronization, complicate intraoperative AI deployment, and disrupt the seamless incorporation of AI into orthopedic workflows (Sherrod et al., 2023). In addition, the predominant dependence on cloud-based computing introduces network latency, which can delay robotic arm responses; in high-precision settings, such delays may heighten the risk of neurovascular injury and undermine the clinical feasibility of AI-assisted interventions evolution.

  generallimitationsevidence 5/5
  Keywords: orthopedic clinical system algorithms technical outputs based systems intraoperative intelligent limitations among models consequently trust
• Artificial intelligence and robotic technologies redefining precision and personalization in orthopedic surgery: a narrative review (2026) · Frontiers in Bioengineering and Biotechnology · doi

  8 Conclusion The integration of robotic technologies and AI represents a paradigm shift in orthopedic surgery, where the convergence of artificial intelligence and robotics is not merely augmenting existing surgical techniques but fundamentally redefining the standards of precision, safety, and personalization in musculoskeletal care. As evidenced by current applications across diagnosis, surgical navigation, postoperative planning, rehabilitation, in technologies offer improving surgical accuracy, minimizing complications, and enabling data-driven, patient-specific decision-making. and tangible benefits intraoperative these demands significant However, realizing the full potential of AI and robotics in orthopedics unresolved confronting challenges. These include the lack of algorithmic transparency, persistent biases within datasets, fragmented healthcare infrastructures, and the absence of unified regulatory frameworks capable of overseeing continuously learning systems. Without rigorous validation and multicenter trials, and without addressing these systemic barriers, the adoption of such technologies risks entrenching disparities rather than advancing equitable care. large-scale, prospective, through Looking ahead, the trajectory of orthopedic innovation is poised to align with the broader digital health agenda: fostering interdisciplinary collaboration, integrating multimodal data through secure and federated platforms, and advancing towards semi-autonomous or autonomous systems that augment rather than replace surgical expertise. Moreover, the integration of AI with emerging technologies—such as digital twins, 5G-enabled remote surgery, and real-time biomechanical modeling—heralds a new era of precision orthopedics that extends beyond the operating theater to encompass lifelong patient monitoring and predictive analytics. To translate these advances into routine clinical practice, future efforts should prioritize prospective multicenter clinical validation, standardized evaluation frameworks for AI-driven surgical systems, and improved interoperability between AI platforms and hospital information infrastructures. In parallel, regulatory bodies and governance healthcare frameworks, training programs for surgeons, and secure data- sharing mechanisms to ensure the safe, ethical, and equitable deployment of AI-enabled orthopedic technologies. institutions must develop clear This narrative review has several limitations. First, as a narrative review, it did not follow a formal systematic review protocol or PRISMA guidelines, and a quantitative synthesis or meta-analysis was not performed. Therefore, the selection of studies may be subject to publication and selection bias. Second, although multiple databases and key literature sources were consulted, the review may not capture all relevant studies, particularly non-English publications or unpublished data. Third, the included studies vary in methodological quality, study design, and clinical validation, and no formal risk-of-bias assessment was conducted. Finally, the field of artificial intelligence and robotic-assisted orthopedic surgery is rapidly evolving, and some technologies and evidence discussed in this review may change as new research emerges. Despite these limitations, this review provides an up-to- date overview of current applications, challenges, and future directions of AI and robotic technologies in orthopedic surgery.

  generallimitationsevidence 5/5
  Keywords: technologies review orthopedic surgical surgery robotic frameworks systems validation clinical integration artificial intelligence robotics precision
• Early Neurosurgical Intervention In Congenital Hydrocephalus: Predictive Biomarkers, Imaging Trends, And The Transformative Role Of Artificial Intelligence (2026) · International Journal of Drug Delivery Technology · doi

  Imaging protocol The validation of AI-assisted prognostic models with the help of large-scale, multicenter, and prospective studies that are diverse in terms of populations and healthcare systems should be the main focus of future research. and biomarker collection will be important for making models more robust and able to apply to different populations. The building up of pediatric neuroimaging repositories that are shared, with the use of federated learning systems to keep patient data confidential, might allow the teaching of models together. standardization The need for further development of explainable AI techniques is crucial since transparency and clinician trust are major issues that need solving in neurosurgery thus especially in high-stakes decisions. The inclusion of longitudinal neurodevelopmental outcomes the predictive models will help in the evaluation of the treatment effectiveness more comprehensively rather than only focusing on short-term surgical success. Moreover, the addition of prenatal imaging and fetal risk prediction models represents a significant path for intervention planning and parental counseling at an earlier stage. in easy-to-use decision-support In relation to global health, the coming years should see a major effort in changing AI-enabled tools to be used in resource-poor environments through low-field imaging compatibility, lightweight computation models, and for clinicians. In the end, whether or not these cutting-edge approaches will provide better outcomes for children with congenital hydrocephalus will be determined by the integration of technological innovation with clinical workflows, ethical standards, and the realities of health systems.

  generalfuture workevidence 5/5
  Keywords: models imaging systems help populations need major outcomes health protocol validation assisted prognostic large scale
• Artificial intelligence in refractive surgery: progress, challenges, and future directions (2026) · Frontiers in Cell and Developmental Biology · doi

  Yuke Ji 1†, Lu Xie 2†, Fangyan Liu 2, Yanwu Xu 3, Weihua Yang 2* and Shaochong Zhang 1,2* 1Shenzhen Eye Hospital, Jinan University, Shenzhen, Guangdong, China, 2Shenzhen Eye Hospital, Shenzhen Eye Medical Center, Southern Medical University, Shenzhen, Guangdong, China, 3School of Future Technology, South China University of Technology, Guangzhou, China The rapid evolution of artificial intelligence (AI) has catalyzed significant advancements in ophthalmology. As refractive surgery necessitates increasing levels of precision and personalization, AI offers pivotal solutions for optimizing clinical outcomes. This review systematically summarizes recent progress in applying machine learning and deep learning models to corneal refractive surgery and implantable collamer lens (ICL) procedures. Specifically, we examine AI’s utility in preoperative candidate screening, personalized surgical planning, and the prediction of postoperative complications. Although AI demonstrates broad prospects for enhancing surgical decision-making, several challenges remain, including data standardization, algorithm interpretability, cross-device compatibility, and ethical considerations. Overall, AI-driven decision-support systems are accelerating the transition of refractive surgery from standardized protocols to data-driven, individualized management, with the potential to enable more intelligent, automated, and precise surgical correction in ophthalmology.

  generalfuture workevidence 5/5
  Keywords: shenzhen refractive surgery china learning university surgical hospital guangdong medical technology artificial intelligence ophthalmology machine
• Artificial intelligence in refractive surgery: progress, challenges, and future directions (2026) · Frontiers in Cell and Developmental Biology · doi

  Overall, the application of AI technology in the field of refractive surgery has made significant progress, with far-reaching impacts on improving surgical accuracy, formulating personalized surgical plans, and predicting postoperative complications. With the rapid development of big data and AI algorithms, the application of AI in refractive surgery will continue to deepen, advancing toward greater precision and intelligence. In the future, the application of AI will be in the following aspects: (1) AI-Driven primarily reflected Frontiers in Cell and Developmental Biology 08 frontiersin.org Ji et al. 10.3389/fcell.2026.1824307 surgical planning approaches will Preoperative Screening and Candidate Selection. In the future, with the accumulation of more high-quality clinical data, AI screening models will be continuously optimized. By integrating diverse examination data, such as corneal topography and corneal thickness, AI will form a more intelligent screening system. AI can not only improve screening accuracy but also significantly enhance diagnostic efficiency, enabling clinicians to identify highrisk patients more quickly and provide them with personalized treatment recommendations. As AI continues to advance in the field of medical image recognition, fully automated preoperative screening is expected to become a reality. This will allow clinicians to obtain comprehensive patient evaluation reports more efficiently, thereby optimizing clinical decision-making and improving the convenience and accuracy of screening. (2) AI- Assisted Surgical Planning and Individualized Treatment. In the future, AI will not only assist clinicians in surgical planning but also enhance the accuracy of surgical recommendations through continuous training and optimization. In complex cases, AI may even provide multiple surgical options. By learning the relationship between extensive preoperative examination data and postoperative refractive outcomes, AI models can accurately predict optimal surgical parameters. Furthermore, AI is expected to refine the calculation methods for surgical parameters and integrate realtime image analysis technology, enabling dynamic adjustments to surgical strategies during the procedure. This will further improve the precision and personalization of refractive surgery. Such intelligent enhance postoperative visual quality and reduce incidence of complications. (3) Application of AI in Predicting Postoperative Complications. The application of AI technology in postoperative management of refractive surgery is progressively deepening, offering new methods to improve postoperative safety and reduce complication rates.

  generalfuture workevidence 5/5
  Keywords: surgical postoperative screening application refractive surgery accuracy technology predicting complications future planning preoperative improve enhance
• Artificial Intelligence and Robotics in General Surgery: Opportunities and Challenges (2026) · Cureus · doi

  We are still far from a world where AI-driven decisions are autonomously executed by robotic devices. What contemporary technical progress has genuinely achieved is catalyzing the digital transformation of healthcare. Digitalization moves healthcare processes onto electronic platforms and allows data to be processed and analyzed by algorithms. That shift enables systematic data collection, which in turn generates feedback to improve future outcomes. Looking ahead, integrating AI with virtual and augmented reality, along with next-generation autonomous robotic systems, could significantly change clinical practice. These innovations remain largely experimental, but research is exploring how they might become part of routine care. Combining AI with multi-omics analysis, robotic navigation, super-resolution imaging, nanoparticle technology, and internet-based remote technologies would greatly expand surgical applications. These advances will enhance surgical precision, safety, and efficiency. Through interdisciplinary collaboration and ongoing innovation, AI is poised to play an increasingly central role in global healthcare systems, serving a diverse range of patients. More than 520 AI/ML algorithms have already been authorized, most in radiology and oncology, but surgery is next. Augmented reality is proving useful in spine tumor surgery, extending beyond spondylectomy and osteotomy. During resection of a grade 1 presacral ganglioneuroma, AR was noted to minimize exposure size and improve resection precision. Studies show AR can effectively visualize tumor outlines and adjacent structures with minimal registration error, pointing to future applications in tumor surgery [107,108]. Mixed reality navigation has been investigated for fracture care: in guidewire placement, AR-assisted methods showed better precision, less radiation exposure, and shorter insertion time because line-of-sight obstructions were reduced. In transforaminal lumbar interbody fusion (TLIF), AR has been shown to improve workflow and reduce errors, as demonstrated in a study of ten patients [108,110]. Future developments may include generative AI for personalized extended reality training, adapting scenarios to the surgeon's experience and case complexity. Highly customized surgical blueprints based on patientspecific anatomy are also likely to become standard. AI-driven simulations will allow virtual practice of complex procedures, reduce patient risk, and help address global shortages in surgical expertise. AI is ushering in a transformative era in surgery, marked by greater precision, personalization, and better outcomes. Through advanced analytics and machine learning, AI can process complex medical images, support real-time decision-making, and enable predictive modeling. These capabilities let surgeons perform complex procedures with increased accuracy and confidence.

  generalfuture workevidence 5/5
  Keywords: reality surgical precision surgery robotic healthcare improve future tumor complex driven algorithms outcomes virtual augmented
• ProtoFlow: interpretable and robust surgical workflow modeling with learned dynamic scene graph prototypes (2026) · International Journal of Computer Assisted Radiology and Surgery · doi

  This work establishes that learning dynamic scene graph pro- totypes is a highly effective strategy for surgical workflow modeling, directly addressing the critical barriers of data scarcity and model opacity. We demonstrated that ProtoFlow achieves competitive accuracy while delivering exceptional robustness in limited-data, few-shot scenarios, significantly lowering obstacles for development in data-scarce clinical settings. Beyond data efficiency, ProtoFlow’s interpretability provides a tangible path toward the transparent AI sys- tems required for clinical adoption. The model automatically discovers clinically meaningful workflow variations, like Capsulorhexis sub-techniques, offering a verifiable founda- tion for analysis. This capability extends to complex, rare events: The model robustly identifies an unexpected Vitrec- tomy phase and provides granular, node-level explanations for complications like an iris prolapse. Such detailed, multi- level explanations are essential for building clinical trust. By uniting prototype-based interpretability with robust repre- sentation learning, ProtoFlow takes an essential step toward more explainable, reliable, and efficient AI solutions for next- generation surgical data science.

  generalfuture workevidence 5/5
  Keywords: model protoflow clinical learning surgical interpretability provides toward like level explanations essential establishes dynamic scene

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