Early warning score and feasible complementary approach using artificial intelligence-based bio-signal monitoring system: a review

Evidence-Based Review and Discussion Points

Background: The field of sports science has been fundamentally transformed by the integration of artificial intelligence (AI) technologies. AI has enabled advances in areas such as performance optimization, training personalization, injury risk assessment and prevention, talent recognition, rehabilitation, athlete monitoring, and wellness optimization. Objective: This review aims to explore the diverse impact of AI in sports science, highlighting advances in AI techniques, the challenges and limitations of integrating AI tools, and the emerging role of AI chatbots in shaping the future of sports research and applications. Methods: The review examines the existing literature on the application of machine learning, deep learning and other AI techniques in various aspects of sports science research and practice. It provides an overview of how these technologies have been used to improve personalized training programs, video analysis, injury risk prediction, talent identification, and rehabilitation. Results: The article describes how AI-powered tools and techniques have revolutionized sports science, enabling personalized, data-driven and efficient approaches to performance optimization. Sophisticated machine learning algorithms such as artificial neural networks, decision trees and support vector machines have been used to develop predictive models for injury risk assessment and prevention, leading to improvements in athletes' wellbeing and long-term performance. AI-driven talent identification and selection processes have also shown promise in recognizing exceptional athletes with greater accuracy. In addition, the integration of AI into athlete monitoring and rehabilitation has led to greater personalization, better decision making and faster return to play. Conclusion: The integration of AI and machine learning techniques into sports science has the potential to transform the field and lead to improved athlete performance, reduced risk of injury, improved well-being, and more personalized and effective interventions. The emergence of AI chatbots expands the applications of these technologies in sports science and offers new opportunities to streamline research processes, provide personalized advice to athletes and support sports medicine and rehabilitation

Incorporating AI in lung cancer management is a disruptive innovation that has improved diagnosis accuracy, prognosis prediction and treatment modalities. In this literature review, we seek to identify the role of artificial intelligence (AI) and machine learning (ML) in lung cancer detection, diagnosis and treatment between 2010 and 2023. A total of 55 studies were selected systematically from databases such as IEEE Xplore, Scopus and PubMed via a PRISMA-based approach. The analysis reveals that artificial intelligence (AI) techniques, specifically convolutional neural networks (CNNs) and natural language processing (NLP), highly improve the precision of initial detection and imaging of lung cancer. Also, CNN distinguishes between benign and malignant nodules, thus aiding early diagnosis and reducing unnecessary biopsies. On the other hand, NLP is utilized to extract relevant clinical information from electronic health records and unstructured medical texts, thereby enhancing the understanding of patient histories and improving treatment planning. Sensitive and specific scores usually higher than standard techniques characterize these technologies. Results show that traditional statistical approaches couldn’t match AI models whose predictive accuracies are outstanding while providing better care to patients through personalised treatment plans. Furthermore, multi-omics data analysis for personalised treatment planning and clinical decision-making optimisation via Clinical Decision Support Systems (CDSS) powered by AI are some ways artificial intelligence has exhibited its potential in this area. Given this, future studies should aim to fine-tune AI algorithms, improve data integration, and address ethical issues promoting responsible use of AI technologies in clinical practice settings. Despite these advances, data quality, model interpretability, and integration into clinical workflows persist. This review demonstrates the demand for continued research and collaboration from different disciplines so that the complete possibilities of AI in fighting lung cancer may be realized.

Evaluation of the efficacy of the National Early Warning Score in predicting in-hospital mortality via the risk stratification

Purpose : This study aimed to examine the clinical progress of patients discharged from intensive care units (ICU) according to the method of proactive rounding, based on clinical decision-making or the National Early Warning Score (NEWS), and to identify the factors influencing their clinical progress. Methods : A total of 627 patients discharged from ICUs were included in this study. Data were collected from electronic medical records following a comprehensive literature review and analyzed using t-tests, χ2-tests, Fisher’s exact test, and logistic regression. Results : The likelihood of ICU readmission was significantly lower when proactive rounding was conducted using the NEWS compared with clinical decision-making (OR=0.40, p =.036). In addition, increases in oxygen demand were significantly reduced when rounding was guided by NEWS rather than the clinical judgment of the Rapid Response Team (OR=0.26, p =.009). Conclusion : These findings suggest that proactive rounding conducted by the Rapid Response Team based on NEWS significantly reduced ICU readmission rates and oxygen demand. Therefore, NEWS-based proactive rounding by nurses may help predict and detect clinical deterioration at an early stage.

This study compares the efficiency of six early warning systems (EWSs) to determine whether the EWS used in most public hospitals in Queensland, Australia, The Queensland Adult Deterioration Detection System (Q-ADDS), is best suited for use in small regional and rural hospitals. In this retrospective case-control study, patients who experienced an in-hospital severe adverse event (index patients) for a 3.5-year period were demographically and diagnostically matched with patients who had uneventful hospital stays (control patients). The EWS efficiency was based on the area under the receiver operator characteristic curve (AUROC) and the number of false and true alerts generated by each EWS. The incidence of severe adverse events was 1.2% of in-hospital patients, and 2500 sets of vital signs were collected from 159 index and 172 control patients. The EWSs were only able to identify approximately half of the index patients. The AUROC was 0.666 to 0.801 and the EWS generated 2.4 to 7.6 false alerts to every true alert per 1000 admissions. The National Early Warning Score had the best ratio of false to true alerts (2.4:1) but was only able to identify 40.8% of deteriorating patients. The Q-ADDS identified 46.5% of the deteriorating patients and had a false to true alert ratio of 3.2:1. When compared with the National Early Warning Score, systems with higher AUROCs (0.744 and 0.801) also had higher proportion of false alerts. None of the alternative EWSs seem to provide marked benefits over Q-ADDS. At present, there is insufficient evidence to replace Q-ADDS with an alternative EWS. Because the EWSs were only able to identify half of the deteriorating patients, EWSs should be used in conjunction with good clinical judgment.

As the new chair of the British Association of Critical Care Nurses I was delighted to be asked to write a guest editorial for Nursing in Critical Care. I have decided to focus this editorial on a topic that I am very passionate about, recognition and response to the deteriorating patient. Historically, several key reports have highlighted sub-optimal management of patients both discharged from intensive care units (ICU) and at risk of deterioration on general wards, with evidence of deficits in their management (Goldhill et al., 1999). The concept of 'Critical Care Without Walls' saw the introduction of early warning scores (EWS) with an associated escalation strategy (often referred to as track and trigger systems) into acute hospitals in 2000 (Department of Health, 2000). Critical Care Outreach teams were born with a role as skilled responders whose input, initiated through the EWS, offer advanced system assessment and rescue of deteriorating patients before irretrievable deterioration and cardiac arrest occurs. Ninety-nine percent of acute hospitals now employ a EWS to monitor deteriorating patients with 97.9% of these linked to a referral protocol (NCEPOD, 2015). Historically a lack of a standardized approach to EWS has introduced variation in methodology and approach resulting in lack of familiarity by clinical staff. The National Early Warning Scoring should now enable an increasingly standardized approach to the management of the acutely ill patient facilitating improvements in education, communication and continuity of care (Royal College of Physicians, 2012). Is there evidence to suggest EWS work? The literature suggests a positive trend towards improved clinical outcomes including cardiopulmonary arrest, mortality, serious adverse events, length of hospital stay, observation frequency and ICU/HDU admission following implementation of an EWS protocol. Vital sign monitoring and completion of both the afferent (the calculation of an early warning score based on physiological parameters) and efferent (the referral, based on the early warning score, of a patient to a rapid response team) arms of the EWS are important in the 'Chain of Survival', representing the illness trajectory of a patient from identification of illness acuity, through to timely, appropriate and effective response and ultimately survival. However, EWS have been described as a 'band aid' for the failure to manage deteriorating patients in hospital (Litvak and Pronovost, 2010). But what does this mean? Do we rely too heavily on completion of a score without exploring the complexities involved in recognition and response? I believe so. There have been numerous high-profile media reports over the last few years reporting poor, inadequate care with links made to unexpected and untimely deaths. So why has there been little improvement in the recognition and response to deteriorating patients over the last decade with sub-optimal care still evident on general wards impacting directly on patient outcome? (Hogan et al., 2012). If the use of EWS and CCOT are seen as the panacea in the management of acutely ill patients why is it not working? What are we still missing? Studies have demonstrated that there is a general trend towards poor compliance with EWS protocols (Odell, 2015) including significant scoring inaccuracy with omitted EWS, missing elements of the EWS and incorrectly calculated EWS. The use of automated EWS can improve scoring accuracy but errors remain (Jones et al., 2011) suggesting the use of technology is not a simple answer. There is a general trend towards inadequate compliance with the efferent limb of the EWS with concerning extended delays to clinical review. Although improvement is demonstrated in clinical response with the use of electronic protocols, non-compliance still occurs at all EWS stages. But why? If we accept that EWS improve patient outcomes why are we not compliant with the protocol? Firstly, it is important to understand the complexity of implementing EWS. This is multi-dimensional and often influenced by local needs and available resources. A systematic realist review by McGaughey et al. (2017) explored factors affecting compliance with EWS and rapid response activation to the deteriorating patient in hospital. The review identified four key themes, ward culture, hierarchical referral systems, workload and staffing resources as factors having a negative impact on the effective implementation of EWS. Firstly, research suggests that documentation remains incomplete is most cases (Ludikhuize et al., 2012). EWS does improve both observation frequency and documentation, but this remains sporadic. Inadequate staffing and skill mix, poor multi-disciplinary teamwork, poor communication, overuse of technology and lack of family input have all been identified as barriers to effective nursing surveillance (Henneman, 2012). Education is essential to understand the potential benefits of EWS and their relationship to improved clinical outcomes. Acute clinical changes are often recognized and acted upon in a timely fashion using automated skill-based behaviour or rule-based behaviour using pattern recognition. However, deterioration can often be subtle and for a prolonged period. This demands knowledge-based behaviour using observation, experience, consultation and cognitive processing. Subjectively the patient's condition may be deteriorating, however the objective measurements are not yet severe enough to activate the EWS. In these situations, response to deterioration is often delayed as staff wait for more objective data to become apparent (Braaten, 2015). Cultural barriers can also affect the decision to act on an EWS. Braaten (2015) identifies informal hierarchical norms in the hospital culture as a constraint to recognition and response. The need to justify escalating the management of a deteriorating patient demands confidence in assessing the patient especially in instances of subtle changes. Not wanting to instigate a false alarm or to appear incompetent and unable to handle the situation have also been identified as barriers. This may be accompanied by a fear of reprisal or criticism of the escalation if it is deemed to be unnecessary. This need for justification leads to delays in treatment and worse clinical outcomes. Shearer et al. (2012) identified the most common reason for failure to respond was that the staff involved felt that they had the clinical situation under control despite an elevated EWS. This suggests that a shift in both education and culture is needed to ensure that staff fully engage with, accept and value the EWS system. So I would suggest the most important question has to be what can we do better? How can we improve recognition and response to these patients? There seems little point in identifying barriers if we do not use those barriers to inform evidence-based change. Some say education, education, education, but this is a one-dimensional view. Simply 'teaching stuff' does not encompass the complexities involved. What about the exploration of the issues raised above: Multi-disciplinary team working, decision making, culture and staffing levels? How can we start to explore these in terms of strategies to improve recognition and response? There is evidence that the lack of effectiveness of interventions such as EWS may be due to the lack of behavioural theory at the design and implementation stage (Davies et al., 2010). Interventions designed using psychological theory have been shown to have larger effects on behaviour than those that do not (Michie and Johnston, 2012). Implementation of patient safety initiatives such as EWS is complex and compliance can be difficult to achieve and sustain. Successful implementation encompasses a range of technical, psychological and socio-cultural factors, which are often not considered in traditional top-down implementation approaches. I would suggest that we need to use a behaviour change approach, underpinned by implementation science, to explore compliance and identify strategies to address the issues that we have identified above.

Many hospitals use early warning scores to help clinicians recognize potentially deteriorating patients and intervene early.Systematic reviews [1][2][3] have identified more than 30 such scores, which vary widely in the methods used for their development and validation.Edelson and colleagues 4 compared 6 early warning scores across more than 362 000 medical-surgical ward encounters in 7 hospitals in the Yale New Haven Health System.They compared 3 statistically advanced scores (eCART, the Rothman Index, and the Epic Deterioration Index) and 3 simpler, points-based scores (National Early Warning Score [NEWS], NEWS2, and Modified Early Warning Score [MEWS]) in their ability to predict ward-to-intensive care unit (ICU) transfer or death within 24 hours of the prediction.Accuracy and the amount of lead time between a high-risk prediction and a deterioration event varied across the scores.In some cases, the simpler scores outperformed more statistically advanced scores.The best performing score was eCART, whereas the Epic Deterioration Index was among the worst performing scores.Despite their widespread use, the evidence base for early warning scores remains surprisingly thin.Many scores have serious methodological flaws or have not been externally validated, and relatively few scores are shared openly. 1 There have been few rigorous evaluations of clinical impact, with only a small number of studies showing improved patient outcomes. 3Thus, despite their promise, there is still substantial uncertainty about which early warning scores should be used and how they should be implemented.The comparative performance of early warning scores is poorly understood because of heterogeneity in the datasets and methods used to develop and validate each score.By benchmarking the performance of early warning scores in a large, multicenter, external dataset, Edelson and colleagues 4 make an important contribution to the literature.Although they compared several commonly used scores, it is unfortunate that many other models are not shared openly and could not also be compared, with the most obvious omission being the Advanced Alert Monitor, which was implemented to reduce 30-day mortality in 21 Kaiser Permanente Northern California hospitals. 5e study's findings somewhat contradict the previous literature.Systematic reviews have found that statistically advanced models, including those that use machine learning, tend to outperform simpler, points-based scores. 2 However, such studies are often conducted in the datasets that are used to train the advanced models and thus may produce optimistic estimates of model performance.In this direct comparison in an external dataset, the statistically advanced scores were not uniformly better than simpler ones.The eCART score was superior across various comparisons, but the simple NEWS and NEWS2 scores performed similarly to the Rothman Index and were better than the Epic Deterioration Index.It is worth noting that eCART was the only model in this study that was based on machine learning.It is a gradient-boosted machine learning model with 97 predictors.In contrast, the Epic Deterioration Index is an ordinal logistic regression model with 17 predictors, and the Rothman Index is a heuristic model that aggregates mortality risk associated with 26 individual variables using advanced statistics but not machine learning.The simpler NEWS and NEWS2 models are also based on logistic regression (with 7 input variables), and the worst-performing model, MEWS, was based on expert consensus and 5 inputs.Although the study's authors 4 describe only the first 3 models as artificial intelligence (AI), it is not clear where this boundary should be drawn or whether this distinction is useful.To understand the performance of a prediction model, it is more helpful to take

BackgroundThe use of early warning scores (EWSs), which integrate real-time vital sign monitoring, can help rapid response teams (RRTs) proactively identify patients at risk of deterioration. However, existing EWSs demonstrate limited evidence for the reduction of clinically important adverse events. The Visensia Safety Index (VSI) is an EWS that combines heart rate, blood pressure, temperature, oxygen saturation, and respiration rate vital sign information to generate a VSI acuity score ranging from 0, signifying the lowest risk of deterioration, to 5, signifying highest risk of deterioration. Continuous monitoring of the risk of deterioration, with alerts triggered by a score of 3.0 or greater, prompts medical attention.ObjectiveThis protocol outlines the methodology for assessing the feasibility of combining a portable continuous vital sign monitoring system (Masimo Root Monitor) with VSI monitoring to evaluate patients at high risk of acute deterioration at The Ottawa Hospital (TOH).MethodsThis 2-phase, prospective cohort study will be conducted at TOH. Patient participants will include adults (≥18 years) with high-risk conditions, such as those undergoing high-risk elective surgery, malignant hematology or oncology patients, and those admitted with infections. Exclusion criteria include patients receiving comfort care or those in specialized units requiring higher-level monitoring. Eligible patients will be monitored using the VSI with alerts to trigger rapid response team evaluation. In tandem, health care workers including physicians, nurses, and research staff who are involved in patient care and monitoring will be recruited for semistructured interviews. These interviews will explore health care providers’ clinical impressions of the VSI as an EWS to identify barriers and enablers to implementation using the Consolidated Framework for Implementation Research. Interviews will assess impressions of the monitoring technology, clinical workflow challenges, and perceptions of implementation. Data analysis will involve directed content analysis of interview transcripts to identify barriers and drivers for successful implementation. Furthermore, the feasibility of the VSI will be assessed by evaluating the proportion of continuous data successfully collected and transmitted, the timeliness of VSI-triggered alerts and their communication to the RRT team, the regular updating of the predictive tool, the rate of reported VSI-triggered events, and the completion rates of study-related tasks by research and clinical staff.ResultsAs of April 2025, we can report that enrollment is complete, and analysis of the results is ongoing. Interviews are still being transcribed and analyzed. We anticipate submission for publication of the results of the study by the summer of 2025.ConclusionsThe execution of this prospective, mixed methods feasibility study requires a multidisciplinary effort. The study uses continuous vital sign monitoring, combined with prediction of deterioration risk, used to activate the RRT. Evaluating the feasibility and clinical impressions of this trial implementation is the first step in exploring monitoring-based predictive decision support.Trial RegistrationClinicalTrials.gov NCT05108376; https://clinicaltrials.gov/study/NCT05108376International Registered Report Identifier (IRRID)DERR1-10.2196/65360

Diabetes mellitus is a chronic metabolic disorder with significant global prevalence and associated healthcare burdens, necessitating early detection and effective management strategies. The integration of Machine Learning (ML) and Artificial Intelligence (AI) has revolutionized diabetes care, offering innovative approaches to prediction, monitoring, and personalized management. This study conducted a systematic review of 82 high-quality peer-reviewed articles, following the PRISMA guidelines, to provide a comprehensive evaluation of ML and AI applications in diabetes prediction and management. The review highlights the widespread adoption of supervised learning models, such as Random Forest and Support Vector Machines (SVM), which consistently demonstrate high accuracy and reliability in predicting diabetes risk. Ensemble learning methods, particularly Gradient Boosting, emerged as superior techniques for predictive performance, while deep learning models, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), proved effective in analyzing unstructured data such as medical images and time-series glucose data. The integration of AI into wearable devices and mobile health applications has further enhanced real-time monitoring and glycemic control, bridging the gap between technological advancements and practical healthcare solutions. Despite these advancements, challenges such as data imbalance, limited external validation, and the need for explainable AI frameworks persist, underscoring the necessity for methodological rigor and standardization. This review provides critical insights into the current state, limitations, and opportunities of ML and AI in diabetes care, emphasizing their transformative potential in addressing this global health challenge.

Risk prediction has been a cornerstone of efforts to prevent clinical deterioration in hospitalized patients.However, despite guideline recommendations for use of early warning scores (EWSs) as part of a rapid response system, substantial questions remain about which scores to use and how best to implement them to reduce in-hospital cardiac arrest and mortality. 1The study by Covino and colleagues 2 adds to those questions by demonstrating that patient age impacts not only the discrimination and calibration of different models but also the weighting of physiologic inputs to the model.In their study comparing EWSs in emergency department patients aged 80 years and older, the authors demonstrated that while the area under the receiver operating characteristic curve for predicting intensive care unit admission or death decreased slightly with increasing age for most scores, the Rapid Emergency Medicine Score (REMS), 1 of only 2 of the tested models that included age, discriminated better in patients older than 94 years and was also the best calibrated of the scores.In addition, they included a variable importance analysis demonstrating several interactions between age and other model inputs, with supplemental oxygen and systolic blood pressure, for example, having more of an impact on risk in patients older than 86 years.This study 2 serves as an important reminder that age is both a key covariate and a potential confounder in clinical deterioration prediction, raising the question of why its inclusion is not more ubiquitous in EWSs.Age is a well-known predictor of mortality, which the developers of the National Early Warning Score (NEWS) were keenly aware of and had previously published on, 3 suggesting that the decision to omit age from NEWS was a methodologic one rather than evidence based.Furthermore, these findings echo broader work comparing machine learning-based EWSs with traditional scores, where models that implicitly account for interactions often achieve higher discrimination. 4aditional EWSs, such as the Modified Early Warning Score and NEWS, were intentionally simple, relying on fixed thresholds, additive points, and transparent interpretation, features that were essential in the era of paper charting and manual calculation.In contrast, modern artificial intelligence and machine learning models add complexity in calculation and interpretability but can be trained to account for interactions between predictors, reflecting the clinical reality that physiology rarely behaves linearly or independently.More importantly, prior work suggests that the success or failure of early warning systems may depend less on model performance than on how predictions are operationalized.Studies of rapid response systems have demonstrated that even well-validated scores may fail to reduce adverse outcomes when alerts are poorly timed, poorly targeted, or lack clear response pathways. 5terpreted literally, the findings from Covino et al 2 could be used to argue that hospitals should use NEWS in younger emergency department patients and REMS in older ones.That approach would retain model simplicity while accounting for age-specific interactions.However, it would also introduce substantial complexity for frontline clinicians, who would need to be familiar with multiple scores and workflows.In this context, adding multiple age-stratified scores may paradoxically worsen performance by increasing cognitive burden and reducing adherence, even if each individual model is statistically optimized for its subgroup.

Artificial intelligence (AI) and machine learning (ML) are emerging as transformative tools in intensive care medicine. Nevertheless, despite the development of numerous AI/ML models, their integration into routine ICU practice remains limited. This concise review examines the role of AI and data science in critical care, with a focus on their contributions to safety and quality assurance, clinical processes improvements, and ICU management. By synthesizing current evidence, this review aims to highlight the opportunities and challenges associated with implementing AI-driven solutions in critical care settings. English-language articles were identified in PubMed using keywords related to AI, ML, ICU management, clinical decision support, and predictive analytics. Original research articles, reviews, letters, and commentaries relevant to AI/ML applications in ICU quality and performance assessment were included. Relevant literature was identified, key findings were synthesized into a structured narrative review. The integration of AI and ML into ICU management leverages vast clinical data to evaluate ICU performance, measure risk factors, optimize workflows, and predict adverse events. ML-driven models can improve clinical decision-making and ICU management. Despite the promising results, real-world implementation requires rigorous validation and clinician adoption. AI-driven successful implementation in ICU comes with significant challenges. AI and ML have the potential to transform ICU management. However, their success depends on validated methodologies, interoperable data frameworks, and interpretable models that clinicians can trust. Advancing AI use in the ICU demands a multidisciplinary effort to create adaptive, transparent, and clinically meaningful solutions that enhance patient care and improve workflow, while ensuring safety and efficiency.

The integration of artificial intelligence (AI) in education has the potential to revolutionize teaching and learning, particularly in the development of students’ critical thinking skills. This study explores science instructors' familiarity, perceptions, and experiences with using AI to enhance students' critical thinking skills, as well as the level of institutional support for AI integration in teaching. A quantitative survey was conducted among 20 science instructors from higher education institutions in Isabela, Philippines. The findings reveal that while instructors acknowledge AI's potential to improve educational outcomes, there is a significant gap in formal AI training and literacy among educators. Positive correlations were found between AI literacy, AI integration, and critical thinking development, suggesting that as AI literacy increases, AI integration and enhancement of critical thinking skills also increase. Regression analysis identified AI integration as a significant predictor of critical thinking development. Challenges remain in the effective implementation of AI, including concerns about overreliance on AI-generated responses and the need for clear assessment guidelines. Interestingly, years of teaching experience did not significantly influence participants’ AI literacy, perceptions, or integration. This study highlights the importance of developing comprehensive AI literacy programs for educators and integrating AI into curriculum structures to balance AI-enhanced learning with human-centered pedagogy. These findings emphasize the need for thoughtful implementation and ongoing research to effectively leverage AI in promoting critical thinking skills in science education.

The next generation 6G era is considered to be highly coupled with intelligent network management and orchestration, while 5G is completely renowned for micro-service architecture-based network cloudification. 6G has been revolutionized for satisfying the mandatory services and carry forwarding the potentialities of 5G to superior and intelligent level. 6G network structure is determined to be dynamic, densely deployed and extremely heterogeneous, and when integrated with a high degree of Quality of Service (QoS) completely transforms the complex architecture into a seamless operating process of classical networks. The immense role of Artificial Intelligence (AI) and Machine Learning (ML) is required for improving the paradigm of 6G for learning information from uncertain and dynamic environments. This integration of AI and 6G resembles a double-edged sword since the application of AI may positively influences the privacy or security of 6G on one side, and negatively introduces the possibility of security infringement into 6G on the other side. In specific, the self-sustaining networks in 6G are obtained by guaranteed application of intelligent security attack mitigation schemes and proactive threat discovery approaches that facilitate end-to-end future network automation. In this Chapter, a comprehensive review of AI and ML-based security enforcement techniques are contributed for improving reliability during robust data dissemination in 6G communications. It presents consolidated and solidified role of AI and ML towards the enforcement of security in 6G networks. In addition, it also demonstrates the challenges and solutions that are handled by the inclusion of AI and ML-based attack mitigation approaches concerning energy and security-based ultra-massive access.

Artificial intelligence (AI) is transforming ophthalmology by leveraging machine learning (ML) and deep learning (DL) techniques, particularly artificial neural networks (ANN) and convolutional neural networks (CNN) to mimic human brain functions and enhance accuracy through data exposure. These AI systems are particularly effective in analyzing ophthalmic images for early disease detection, improving diagnostic precision, streamlining clinical workflows, and ultimately enhancing patient outcomes. This study aims to explore the specific applications and impact of AI in the fields of glaucoma, corneal diseases, and oculoplastics. This study reviews current AI technologies in ophthalmology, examining the implementation of ML and DL techniques. It evaluates AI's role in early disease detection, diagnostic accuracy, clinical workflow enhancement, and patient outcomes. AI has significantly advanced the early detection and management of various ocular conditions. In glaucoma, AI systems provide standardized, rapid identification of disease characteristics, reducing intra- and interobserver bias and workload. For corneal diseases, AI tools enhance diagnostic methods for conditions such as keratitis and keratoconus, improving early detection and treatment planning. In oculoplastics, AI assists in the diagnosis and monitoring of eyelid and orbital diseases, facilitating precise surgical planning and postoperative management. The integration of AI in ophthalmology has revolutionized eye care by enhancing diagnostic precision, streamlining clinical workflows, and improving patient outcomes. As AI technologies continue to evolve, their applications in ophthalmology are expected to expand, offering innovative solutions for the diagnosis, monitoring, treatment, and surgical outcomes of various eye conditions.