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Significance: Advancements in label-free microscopy could provide real-time, non-invasive imaging with unique sources of contrast and automated standardized analysis to characterize heterogeneous and dynamic biological processes. These tools would overcome challenges with widely used methods that are destructive (e.g., histology, flow cytometry) or lack cellular resolution (e.g., plate-based assays, whole animal bioluminescence imaging). Aim: This perspective aims to (1) justify the need for label-free microscopy to track heterogeneous cellular functions over time and space within unperturbed systems and (2) recommend improvements regarding instrumentation, image analysis, and image interpretation to address these needs. Approach: Three key research areas (cancer research, autoimmune disease, and tissue and cell engineering) are considered to support the need for label-free microscopy to characterize heterogeneity and dynamics within biological systems. Based on the strengths (e.g., multiple sources of molecular contrast, non-invasive monitoring) and weaknesses (e.g., imaging depth, image interpretation) of several label-free microscopy modalities, improvements for future imaging systems are recommended. Conclusion: Improvements in instrumentation including strategies that increase resolution and imaging speed, standardization and centralization of image analysis tools, and robust data validation and interpretation will expand the applications of label-free microscopy to study heterogeneous and dynamic biological systems.

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Antecedentes: La escala Teacher Emotion Inventory (TEI) es un instrumento que evalúa emociones discretas experimentadas por el profesorado en el proceso de enseñanza-aprendizaje. El objetivo de este estudio es examinar las propiedades psicométricas de la versión breve española de la escala Teacher Emotion Inventory (TEI-BSV) en una muestra de 567 profesores (65.5% son mujeres), con edades comprendidas entre 25 y 65 años (M = 46.04; DT = 9.09). Método: Tras su adaptación mediante traducción inversa, el profesorado completó una batería que incluía el TEI-BSV, un cuestionario de inteligencia emocional, dos escalas de bienestar subjetivo, una escala sobre burnout y una escala sobre engagement. Resultados: Los resultados mostraron una consistencia interna adecuada de las subescalas del TEI-BSV. Los análisis factoriales (exploratorio y confirmatorio) proporcionaron pruebas de que el TEI-BSV tiene una estructura de cuatro factores con un buen ajuste, frente a la estructura de cinco factores original. Se han hallado evidencias de validez convergente, así como de validez criterial e incremental del TEI-BSV. Conclusiones: el TEI-BSV podría ser una herramienta útil para la evaluación ecológica de las emociones discretas del profesorado en su contexto laboral.(AU)

Background: The Teacher Emotion Inventory (TEI) scale is an instrument that evaluates discrete emotions experienced by teachers in the teaching-learning process. The aim of this study was to examine the psychometric properties of the brief Spanish version of the Teacher Emotion Inventory scale (TEI-BSV) using a sample of 567 teachers (65.5% women), aged between 25 and 65 years (M= 46.04; SD= 9.09). Methods: After adaptation through back-translation, the teachers com-pleted a battery of tests included in the TEI-BSV: an emotional intelli-gence questionnaire, two subjective well-being scales, a burnout scale and a scale on engagement. Results: The data revealed adequate internal consistency of the TEI-BSV subscales, and exploratory and confirma-tory factor analyses provided evidence that the TEI-BSV has a four-factor structure with good adjustment, as opposed to the original five-factor structure proposed. There was evidence of convergent validity of the TEI-BSV, as well as criterion and incremental validity. Conclusions: The TEI-BSV could be a useful instrument for the ecological assess-ment of teachers' discrete emotions in the context of their workplace.(AU)

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OBJECTIVE: To develop a deep learning algorithm for diagnosing lumbar central canal stenosis (LCCS) using abdominal CT (ACT) and lumbar spine CT (LCT). MATERIALS AND METHODS: This retrospective study involved 109 patients undergoing LCTs and ACTs between January 2014 and July 2021. The dural sac on CT images was manually segmented and classified as normal or stenosed (dural sac cross-sectional area ≥ 100 mm2 or < 100 mm2, respectively). A deep learning model based on U-Net architecture was developed to automatically segment the dural sac and classify the central canal stenosis. The classification performance of the model was compared on a testing set (990 images from 9 patients). The accuracy, sensitivity, and specificity of automatic segmentation were quantitatively evaluated by comparing its Dice similarity coefficient (DSC) and intraclass correlation coefficient (ICC) with those of manual segmentation. RESULTS: In total, 990 CT images from nine patients (mean age ± standard deviation, 77 ± 7 years; six men) were evaluated. The algorithm achieved high segmentation performance with a DSC of 0.85 ± 0.10 and ICC of 0.82 (95% confidence interval [CI]: 0.80,0.85). The ICC between ACTs and LCTs on the deep learning algorithm was 0.89 (95%CI: 0.87,0.91). The accuracy of the algorithm in diagnosing LCCS with dichotomous classification was 84%(95%CI: 0.82,0.86). In dataset analysis, the accuracy of ACTs and LCTs was 85%(95%CI: 0.82,0.88) and 83%(95%CI: 0.79,0.86), respectively. The model showed better accuracy for ACT than LCT. CONCLUSION: The deep learning algorithm automatically diagnosed LCCS on LCTs and ACTs. ACT had a diagnostic performance for LCCS comparable to that of LCT.

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This chapter primarily focuses on the progress in depression precision medicine with specific emphasis on the integrative approaches that include artificial intelligence and other data, tools, and technologies. After the description of the concept of precision medicine and a comparative introduction to depression precision medicine with cancer and epilepsy, new avenues of depression precision medicine derived from integrated artificial intelligence and other sources will be presented. Additionally, less advanced areas, such as comorbidity between depression and cancer, will be examined.

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INTRODUCTION: Accurate and rapid triage can reduce undertriage and overtriage, which may improve emergency department flow. This study aimed to identify the effects of a prospective study applying artificial intelligence-based triage in the clinical field. DESIGN: Systematic review of prospective studies. METHODS: CINAHL, Cochrane, Embase, PubMed, ProQuest, KISS, and RISS were searched from March 9 to April 18, 2023. All the data were screened independently by three researchers. The review included prospective studies that measured outcomes related to AI-based triage. Three researchers extracted data and independently assessed the study's quality using the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) protocol. RESULTS: Of 1633 studies, seven met the inclusion criteria for this review. Most studies applied machine learning to triage, and only one was based on fuzzy logic. All studies, except one, utilized a five-level triage classification system. Regarding model performance, the feed-forward neural network achieved a precision of 33% in the level 1 classification, whereas the fuzzy clip model achieved a specificity and sensitivity of 99%. The accuracy of the model's triage prediction ranged from 80.5% to 99.1%. Other outcomes included time reduction, overtriage and undertriage checks, mistriage factors, and patient care and prognosis outcomes. CONCLUSION: Triage nurses in the emergency department can use artificial intelligence as a supportive means for triage. Ultimately, we hope to be a resource that can reduce undertriage and positively affect patient health. PROTOCOL REGISTRATION: We have registered our review in PROSPERO (registration number: CRD 42023415232).

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OBJECTIVE: The aim of this study is to assess the feasibility and implementation of a novel approach for intraoperative brain smears within the operating room, which is augmented with deep learning technology. Materials and methods: This study is designed as an observational to evaluate the feasibility and implementation of using an innovative approach to intraoperative brain smears within the operating room, augmented with deep learning technology. The study will be conducted at Aga Khan University Hospital in Karachi, Pakistan, from May 2024 to July 2026, with an estimated sample size of 258. A neurosurgical trainee, trained by the study neuropathologist, will prepare and examine the smears under a microscope in the operating room. The findings of the trainee will be documented and compared to routine intraoperative consultations (smear and/or frozen section) and final histopathology results obtained from the pathology department. Additionally, the study will incorporate artificial intelligence tools to assist with the interpretation of smear and a telepathology interface to enable consultation from an off-site neuropathologist. CONCLUSIONS: The results of this study will hold significant potential to revolutionise neurosurgery practices in lowand middle-income countries by introducing a cost-effective, efficient, and high-quality intraoperative consultation method to settings that currently lack the necessary infrastructure and expertise. The implementation of this innovative approach has the potential to improve patient outcomes and increase access to intraoperative diagnosis, thereby addressing a significant unmet need in LMICs.

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Brain tumour diagnosis involves assessing various radiological and histopathological parameters. Imaging modalities are an excellent resource for disease monitoring. However, manual inspection of imaging is laborious, and performance varies depending on expertise. Artificial Intelligence (AI) driven solutions a non-invasive and low-cost technology for diagnostics compared to surgical biopsy and histopathological diagnosis. We analysed various machine learning models reported in the literature and assess its applicability to improve neuro-oncological management. A scoping review of 47 full texts published in the last 3 years pertaining to the use of machine learning for the management of different types of gliomas where radiomics and radio genomic models have proven to be useful. Use of AI in conjunction with other factors can result in improving overall neurooncological management within LMICs. AI algorithms can evaluate medical imaging to aid in the early detection and diagnosis of brain tumours. This is especially useful where AI can deliver reliable and efficient screening methods, allowing for early intervention and treatment.

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PURPOSE: The differences between the Alberta Stroke Program Early CT Score (ASPECTS) obtained by experts and artificial intelligence (AI) software require elucidation. We aimed to characterize the discrepancies between the ASPECTS obtained by AI and experts and determine the associated factors and prognostic implications. METHODS: This multicenter, retrospective, observational cohort study included patients showing acute ischemic stroke caused by large-vessel occlusion in the anterior circulation. ASPECTS was determined by AI software (RAPID ASPECTS) and experts from the core laboratory. Interclass correlation coefficients (ICCs) and Bland-Altman plots were used to illustrate the consistency and discrepancies; logistic regression analyses were used to assess the correlates of inconsistency; and receiver operating characteristic analyses were performed to assess the diagnostic performance for predicting unfavorable clinical outcomes. RESULTS: The study population included 491 patients. The ICC for the expert and AI ASPECTS was 0.63 (95 % confidence interval [CI]: 0.25-0.79).The mean difference between expert and AI ASPECTS was 2.24. Chronic infarcts (odds ratio [OR], 1.9; 95 % CI, 1.1-3.4; P=0.021) and expert scores in the internal capsule (OR, 2.9; 95 % CI, 1.1-7.7; P=0.034) and lentiform (OR, 2.4; 95 % CI, 1.3-4.7; P=0.008) were significant correlates of inconsistency. The ASPECTS obtained by AI showed a significantly higher area under the curve for unfavorable outcomes (0.68 vs. 0.63, P=0.04). CONCLUSIONS: In comparison with expert ASPECTS, AI ASPECTS overestimated the infarct extent. Future studies should aim to determine whether AI ASPECTS assessments should use a lower threshold to screen patients for endovascular therapy.

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INTRODUCTION: Missed fractures are the most frequent diagnostic error attributed to clinicians in UK emergency departments and a significant cause of patient morbidity. Recently, advances in computer vision have led to artificial intelligence (AI)-enhanced model developments, which can support clinicians in the detection of fractures. Previous research has shown these models to have promising effects on diagnostic performance, but their impact on the diagnostic accuracy of clinicians in the National Health Service (NHS) setting has not yet been fully evaluated. METHODS AND ANALYSIS: A dataset of 500 plain radiographs derived from Oxford University Hospitals (OUH) NHS Foundation Trust will be collated to include all bones except the skull, facial bones and cervical spine. The dataset will be split evenly between radiographs showing one or more fractures and those without. The reference ground truth for each image will be established through independent review by two senior musculoskeletal radiologists. A third senior radiologist will resolve disagreements between two primary radiologists. The dataset will be analysed by a commercially available AI tool, BoneView (Gleamer, Paris, France), and its accuracy for detecting fractures will be determined with reference to the ground truth diagnosis. We will undertake a multiple case multiple reader study in which clinicians interpret all images without AI support, then repeat the process with access to AI algorithm output following a 4-week washout. 18 clinicians will be recruited as readers from four hospitals in England, from six distinct clinical groups, each with three levels of seniority (early-stage, mid-stage and later-stage career). Changes in the accuracy, confidence and speed of reporting will be compared with and without AI support. Readers will use a secure web-based DICOM (Digital Imaging and Communications in Medicine) viewer (www.raiqc.com), allowing radiograph viewing and abnormality identification. Pooled analyses will be reported for overall reader performance as well as for subgroups including clinical role, level of seniority, pathological finding and difficulty of image. ETHICS AND DISSEMINATION: The study has been approved by the UK Healthcare Research Authority (IRAS 310995, approved on 13 December 2022). The use of anonymised retrospective radiographs has been authorised by OUH NHS Foundation Trust. The results will be presented at relevant conferences and published in a peer-reviewed journal. TRIAL REGISTRATION NUMBERS: This study is registered with ISRCTN (ISRCTN19562541) and ClinicalTrials.gov (NCT06130397). The paper reports the results of a substudy of STEDI2 (Simulation Training for Emergency Department Imaging Phase 2).

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Infectious diseases, driven by a diverse array of pathogens, can swiftly undermine public health systems. Accurate diagnosis and treatment of infectious diseases-centered around the identification of biomarkers and the elucidation of disease mechanisms-are in dire need of more versatile and practical analytical approaches. Mass spectrometry (MS)-based molecular profiling methods can deliver a wealth of information on a range of functional molecules, including nucleic acids, proteins, and metabolites. While MS-driven omics analyses can yield vast datasets, the sheer complexity and multi-dimensionality of MS data can significantly hinder the identification and characterization of functional molecules within specific biological processes and events. Artificial intelligence (AI) emerges as a potent complementary tool that can substantially enhance the processing and interpretation of MS data. AI applications in this context lead to the reduction of spurious signals, the improvement of precision, the creation of standardized analytical frameworks, and the increase of data integration efficiency. This critical review emphasizes the pivotal roles of MS based omics strategies in the discovery of biomarkers and the clarification of infectious diseases. Additionally, the review underscores the transformative ability of AI techniques to enhance the utility of MS-based molecular profiling in the field of infectious diseases by refining the quality and practicality of data produced from omics analyses. In conclusion, we advocate for a forward-looking strategy that integrates AI with MS-based molecular profiling. This integration aims to transform the analytical landscape and the performance of biological molecule characterization, potentially down to the single-cell level. Such advancements are anticipated to propel the development of AI-driven predictive models, thus improving the monitoring of diagnostics and therapeutic discovery for the ongoing challenge related to infectious diseases.

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OBJECTIVES: This study aims to investigate the cost-effectiveness of individually tailored self-management support, delivered via the artificial intelligence-based selfBACK app, as an add-on to usual care for people with low back pain (LBP). DESIGN: Secondary health-economic analysis of the selfBACK randomised controlled trial (RCT) with a 9-month follow-up conducted from a Danish national healthcare perspective (primary scenario) and a societal perspective limited to long-term productivity in the form of long-term absenteeism (secondary scenario). SETTING: Primary care and an outpatient spine clinic in Denmark. PARTICIPANTS: A subset of Danish participants in the selfBACK RCT, including 297 adults with LBP randomised to the intervention (n=148) or the control group (n=149). INTERVENTIONS: App-delivered evidence-based, individually tailored self-management support as an add-on to usual care compared with usual care alone among people with LBP. OUTCOME MEASURES: Costs of healthcare usage and productivity loss, quality-adjusted life-years (QALYs) based on the EuroQol-5L Dimension Questionnaire, meaningful changes in LBP-related disability measured by the Roland-Morris Disability Questionnaire (RMDQ) and the Pain Self-Efficacy Questionnaire (PSEQ), costs (healthcare and productivity loss measured in Euro) and incremental cost-effectiveness ratios (ICERs). RESULTS: The incremental costs were higher for the selfBACK intervention (mean difference €230 (95% CI -136 to 595)), where ICERs showed an increase in costs of €7336 per QALY gained in the intervention group, and €1302 and €1634 for an additional person with minimal important change on the PSEQ and RMDQ score, respectively. At a cost-effectiveness threshold value of €23250, the selfBACK intervention has a 98% probability of being cost-effective. Analysis of productivity loss was very sensitive, which creates uncertainty about the results from a societal perspective limited to long-term productivity. CONCLUSIONS: From a healthcare perspective, the selfBACK intervention is likely to represent a cost-effective treatment for people with LBP. However, including productivity loss introduces uncertainty to the results. TRIAL REGISTRATION NUMBER: NCT03798288.

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This paper presents the design and development of a coastal fisheries monitoring system that harnesses artificial intelligence technologies. Application of the system across the Pacific region promises to revolutionize coastal fisheries management. The program is built on a centralized, cloud-based monitoring system to automate data extraction and analysis processes. The system leverages YoloV4, OpenCV, and ResNet101 to extract information from images of fish and invertebrates collected as part of in-country monitoring programs overseen by national fisheries authorities. As of December 2023, the system has facilitated automated identification of over six hundred nearshore finfish species, and automated length and weight measurements of more than 80,000 specimens across the Pacific. The system integrates other key fisheries monitoring data such as catch rates, fishing locations and habitats, volumes, pricing, and market characteristics. The collection of these metrics supports much needed rapid fishery assessments. The system's co-development with national fisheries authorities and the geographic extent of its application enables capacity development and broader local inclusion of fishing communities in fisheries management. In doing so, the system empowers fishers to work with fisheries authorities to enable data-informed decision-making for more effective adaptive fisheries management. The system overcomes historically entrenched technical and financial barriers in fisheries management in many Pacific island communities.

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BACKGROUND: The integration of telemedicine in pain management represents a significant advancement in healthcare delivery, offering opportunities to enhance patient access to specialized care, improve satisfaction, and streamline chronic pain management. Despite its growing adoption, there remains a lack of comprehensive data on its utilization in pain therapy, necessitating a deeper understanding of physicians' perspectives, experiences, and challenges. METHODS: A survey was conducted in Italy between January 2024 and May 2024. Specialist center members of the SIAARTI were sent an online questionnaire testing the state of the art of telemedicine for pain medicine. RESULTS: One-hundred thirty-one centers across Italy reveal varied adoption rates, with 40% routinely using telemedicine. Regional disparities exist, with Northern Italy showing higher adoption rates. Barriers include the absence of protocols, resource constraints, and bureaucratic obstacles. Despite challenges, telemedicine has shown positive impacts on service delivery, with increased service volume reported. Technological capabilities, including image sharing and teleconsultation with specialists, indicate promising interdisciplinary potential. CONCLUSIONS: The integration of advanced telemedicine software utilizing artificial intelligence holds promise for enhancing telemonitoring and alert systems, potentially leading to more proactive and personalized pain management strategies.

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OBJECTIVE: Performing obstetric ultrasound scans is challenging for inexperienced operators; therefore, the prenatal screening artificial intelligence system (PSAIS) software was developed to provide real-time feedback and guidance for trainees during their scanning procedures. The aim of this study was to investigate the potential benefits of utilizing such an artificial intelligence system to enhance the efficiency of obstetric ultrasound training in acquiring and interpreting standard basic views. METHODS: A prospective, single-center randomized controlled study was conducted at The First Affiliated Hospital of Sun Yat-sen University. From September 2022 to April 2023, residents with no prior obstetric ultrasound experience were recruited and assigned randomly to either a PSAIS-assisted training group or a conventional training group. Each trainee underwent a four-cycle practical scan training program, performing 20 scans in each cycle on pregnant volunteers at 18-32 gestational weeks, focusing on acquiring and interpreting standard basic views. At the end of each cycle, a test scan evaluated trainees' ability to obtain standard ultrasound views without PSAIS assistance, and image quality was rated by both the trainees themselves and an expert (in a blinded manner). The primary outcome was the number of training cycles required for each trainee to meet a certain standard of proficiency (i.e. end-of-cycle test scored by the expert at ≥ 80%). Secondary outcomes included the expert ratings of the image quality in each trainee's end-of-cycle test and the discordance between ratings by trainees and the expert. RESULTS: In total, 32 residents and 1809 pregnant women (2720 scans) were recruited for the study. The PSAIS-assisted trainee group required significantly fewer training cycles compared with the non-PSAIS-assisted group to meet quality requirements (P = 0.037). Based on the expert ratings of image quality, the PSAIS-assisted training group exhibited superior ability in acquiring standard imaging views compared with the conventional training group in the third (P = 0.012) and fourth (P < 0.001) cycles. In both groups, the discordance between trainees' ratings of the quality of their own images and the expert's ratings decreased with increasing training time. A statistically significant difference in overall trainee-expert rating discordance between the two groups emerged at the end of the first training cycle and remained at every cycle thereafter (P < 0.013). CONCLUSION: By assisting inexperienced trainees in obtaining and interpreting standard basic obstetric scanning views, the use of artificial intelligence-assisted systems has the potential to improve training effectiveness. © 2024 International Society of Ultrasound in Obstetrics and Gynecology.

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Emergency Department (ED) presentations for Mental Health (MH) help-seeking have been rising rapidly, with EDs as the main entry point for most individuals in Australia. The objective of this retrospective cohort study was to analyse the sociodemographic and presentation features of people who sought mental healthcare in two EDs located in a regional coastal setting in New South Wales (NSW), Australia from 2016 to 2021. This article is a part of a broader research study on the utilisation of machine learning in MH. The objective of this study is to identify the factors that lead to the admission of individuals to an MH inpatient facility when they seek MH care in an ED. Data were collected using existing records and analysed using descriptive univariate analysis with statistical significance between the two sites was determined using Chi squared test, p < 0.05. Two main themes characterise dominant help-seeking dynamics for MH conditions in ED, suicidal ideation, and access and egress pathways. The main findings indicate that suicidal ideation was the most common presenting problem (38.19%). People presenting to ED who 'Did not wait' or 'Left at own risk' accounted for 10.20% of departures from ED. A large number of presentations arrived via the ambulance, accounting for 45.91%. A large proportion of presentations are related to a potentially life-threatening condition (suicidal ideation). The largest proportion of triage code 1 'Resuscitation' was for people with presenting problem of 'Behavioural Disturbance'. Departure and arrival dynamics need to be better understood in consultation with community and lived experience groups to improve future service alignment with the access and egress pathways for emergency MH care.

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Pressure ulcers (PU) are a globally recognised healthcare concern, with their largely preventable development prompting the implementation of targeted preventive strategies. Risk assessment is the first step to planning individualised preventive measures. However, despite the long use of risk assessment, and the >70 risk assessment tools currently available, PUs remain a significant concern. Various technological advancements, including artificial intelligence, subepidermal moisture measurement, cytokine measurement, thermography and ultrasound are emerging as promising tools for PU detection, and subsequent prevention of more serious PU damage. Given the rise in availability of these technologies, this advances the question of whether our current approaches to PU prevention can be enhanced with the use of technology. This article delves into these technologies, suggesting that they could lead healthcare in the right direction, toward optimal assessment and adoption of focused prevention strategies.

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Background: Artificial intelligence (AI) can enhance life experiences and present challenges for people with disabilities. Objectives: This study aims to investigate the relationship between AI and disability, exploring the potential benefits and challenges of using AI for people with disabilities. Methods: A systematic scoping review was conducted using eight online databases; 45 scholarly articles from the last 5 years were identified and selected for thematic analysis. Results: The review's findings revealed AI's potential to enhance healthcare; however, it showed a high prevalence of a narrow medical model of disability and an ableist perspective in AI research. This raises concerns about the perpetuation of biases and discrimination against individuals with disabilities in the development and deployment of AI technologies. Conclusion: We recommend shifting towards a social model of disability, promoting interdisciplinary collaboration, addressing AI bias and discrimination, prioritizing privacy and security in AI development, focusing on accessibility and usability, investing in education and training, and advocating for robust policy and regulatory frameworks. The review emphasizes the urgent need for further research to ensure that AI benefits all members of society equitably and that future AI systems are designed with inclusivity and accessibility as core principles.

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Interactions of biological molecules in organisms are considered to be primary factors for the lifecycle of that organism. Various important biological functions are dependent on such interactions and among different kinds of interactions, the protein DNA interactions are very important for the processes of transcription, regulation of gene expression, DNA repairing and packaging. Thus, keeping the knowledge of such interactions and the sites of those interactions is necessary to study the mechanism of various biological processes. As experimental identification through biological assays is quite resource-demanding, costly and error-prone, scientists opt for the computational methods for efficient and accurate identification of such DNA-protein interaction sites. Thus, herein, we propose a novel and accurate method namely DeepDBS for the identification of DNA-binding sites in proteins, using primary amino acid sequences of proteins under study. From protein sequences, deep representations were computed through a one-dimensional convolution neural network (1D-CNN), recurrent neural network (RNN) and long short-term memory (LSTM) network and were further used to train a Random Forest classifier. Random Forest with LSTM-based features outperformed the other models, as well as the existing state-of-the-art methods with an accuracy score of 0.99 for self-consistency test, 10-fold cross-validation, 5-fold cross-validation, and jackknife validation while 0.92 for independent dataset testing. It is concluded based on results that the DeepDBS can help accurate and efficient identification of DNA binding sites (DBS) in proteins.

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Accurately diagnosing rare pediatric diseases frequently represent a clinical challenge due to their complex and unusual clinical presentations. Here, we explore the capabilities of three large language models (LLMs), GPT-4, Gemini Pro, and a custom-built LLM (GPT-4 integrated with the Human Phenotype Ontology [GPT-4 HPO]), by evaluating their diagnostic performance on 61 rare pediatric disease case reports. The performance of the LLMs were assessed for accuracy in identifying specific diagnoses, listing the correct diagnosis among a differential list, and broad disease categories. In addition, GPT-4 HPO was tested on 100 general pediatrics case reports previously assessed on other LLMs to further validate its performance. The results indicated that GPT-4 was able to predict the correct diagnosis with a diagnostic accuracy of 13.1%, whereas both GPT-4 HPO and Gemini Pro had diagnostic accuracies of 8.2%. Further, GPT-4 HPO showed an improved performance compared with the other two LLMs in identifying the correct diagnosis among its differential list and the broad disease category. Although these findings underscore the potential of LLMs for diagnostic support, particularly when enhanced with domain-specific ontologies, they also stress the need for further improvement prior to integration into clinical practice.

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The brain-computer interface (BCI) systems based on motor imagery typically rely on a large number of electrode channels to acquire information. The rational selection of electroencephalography (EEG) channel combinations is crucial for optimizing computational efficiency and enhancing practical applicability. However, evaluating all potential channel combinations individually is impractical. This study aims to explore a strategy for quickly achieving a balance between maximizing channel reduction and minimizing precision loss. To this end, we developed a spatio-temporal attention perception network named STAPNet. Based on the channel contributions adaptively generated by its subnetwork, we propose an extended step bi-directional search strategy that includes variable ratio channel selection (VRCS) and strided greedy channel selection (SGCS), designed to enhance global search capabilities and accelerate the optimization process. Experimental results show that on the High Gamma and BCI Competition IV 2a public datasets, the framework respectively achieved average maximum accuracies of 91.47% and 84.17%. Under conditions of zero precision loss, the average number of channels was reduced by a maximum of 87.5%. Additionally, to investigate the impact of neural information loss due to channel reduction on the interpretation of complex brain functions, we employed a heatmap visualization algorithm to verify the universal importance and complete symmetry of the selected optimal channel combination across multiple datasets. This is consistent with the brain's cooperative mechanism when processing tasks involving both the left and right hands.