UNASSIGNED: Retinitis pigmentosa (RP) is an inherited retinal dystrophy characterized by progressive vision loss due to photoreceptor degeneration. Complicated cataract formation, particularly posterior subcapsular cataract (PSC), frequently occurs in RP and exacerbates the visual impairment. Cataract surgery may improve vision; however, the distinctive challenges of RP require specific considerations. This mini-review aims to provide a comprehensive overview of the RP-related cataract.
UNASSIGNED: A comprehensive literature review was conducted via PubMed/MEDLINE, spanning the period from January 1976 to June 2024, using the keywords \"cataract,\" \"cataract surgery,\" \"cystoid macular edema,\" \"hereditary retinal dystrophy,\" \"retinitis pigmentosa,\" \"posterior subcapsular cataract,\" \"posterior capsular opacification,\" \"zonular weakness,\" and \"artificial intelligence.\" We aimed to evaluate cataract surgery in patients with RP, focusing on cataract formation, its surgical management, postoperative complications, patient follow-up, and visual outcomes. Relevant review articles, clinical trials, and case reports with related reference lists of these articles were included.
UNASSIGNED: A total of 53 articles were examined in detail, including those identified through focused keyword searches and the reference lists of these articles. Cataract surgery in patients with RP generally results in substantial visual improvement. However, surgery can be complicated, particularly by zonular weakness and subluxation of the crystalline lens. These risks can be reduced by using capsular tension rings and employing meticulous surgical technique. Furthermore, postoperative complications, such as cystoid macular edema and posterior capsular opacification, are common. Despite these challenges, regular postoperative follow-up and appropriate management can help mitigate complications. Integrity of the ellipsoid zone and external limiting membrane on preoperative optical coherence tomographic examination are the main predictors of visual outcomes following cataract surgery; however, outcomes can vary. Though many patients experience significant visual improvement, some may experience limited benefits due to pre-existing advanced retinal degeneration.
UNASSIGNED: Cataract surgery may offer meaningful visual benefits in patients with RP; however, careful preoperative evaluation and meticulous surgical technique are required to address the possible challenges. Attentive postoperative care and follow-up are essential to optimize visual outcomes. Early surgical intervention can significantly improve the quality of life in selected candidates, and tailored approaches are necessary in patients with RP requiring cataract surgery. Further studies on the potential application of artificial intelligence to monitor postoperative recovery and detect complications may improve surgical outcomes and enhance patient care.

Cutaneous fungal infections are one of the most common skin conditions, hence, the burden of determining fungal elements upon microscopic examination with periodic acid-Schiff (PAS) and Gomori methenamine silver (GMS) stains, is very time consuming. Despite some morphological variability posing challenges to training artificial intelligence (AI)-based solutions, these structures are favored potential targets, enabling the recruitment of promising AI-based technologies. Herein, we present a novel AI solution for identifying skin fungal infections, potentially providing a decision support system for pathologists. Skin biopsies of patients diagnosed with a cutaneous fungal infection at the Sheba Medical Center, Israel between 2014 and 2023, were used. Samples were stained with PAS and GMS and digitized by the Philips IntelliSite scanner. DeePathology® STUDIO fungal elements were annotated and deemed as ground truth data after an overall revision by two specialist pathologists. Subsequently, they were used to create an AI-based solution, which has been further validated in other regions of interests. The study participants were divided into two cohorts. In the first cohort, the overall sensitivity of the algorithm was 0.8, specificity 0.97, F1 score 0.78; in the second, the overall sensitivity of the algorithm was 0.93, specificity 0.99, F1 score 0.95. The results obtained are encouraging as proof of concept for an AI-based fungi detection algorithm. DeePathology® STUDIO can be employed as a decision support system for pathologists when diagnosing a cutaneous fungal infection using PAS and GMS stains, thereby, saving time and money.

OBJECTIVE: We aimed to evaluate the feasibility of using ChatGPT as programming support for nursing PhD students conducting analyses using the All of Us Researcher Workbench.
METHODS: 9 students in a PhD-level nursing course were prospectively randomized into 2 groups who used ChatGPT for programming support on alternating assignments in the workbench. Students reported completion time, confidence, and qualitative reflections on barriers, resources used, and the learning process.
RESULTS: The median completion time was shorter for novices and certain assignments using ChatGPT. In qualitative reflections, students reported ChatGPT helped generate and troubleshoot code and facilitated learning but was occasionally inaccurate.
CONCLUSIONS: ChatGPT provided cognitive scaffolding that enabled students to move toward complex programming tasks using the All of Us Researcher Workbench but should be used in combination with other resources.
CONCLUSIONS: Our findings support the feasibility of using ChatGPT to help PhD nursing students use the All of Us Researcher Workbench to pursue novel research directions.

UNASSIGNED: Heart failure with preserved ejection fraction (HFpEF) is the predominant form of HF in older adults. It represents a heterogenous clinical syndrome that is less well understood across different ethnicities.
UNASSIGNED: This study aimed to compare the clinical presentation and assess the diagnostic performance of existing HFpEF diagnostic tools between ethnic groups.
UNASSIGNED: A validated Natural Language Processing (NLP) algorithm was applied to the electronic health records of a large London hospital to identify patients meeting the European Society of Cardiology criteria for a diagnosis of HFpEF. NLP extracted patient demographics (including self-reported ethnicity and socioeconomic status), comorbidities, investigation results (N-terminal pro-B-type natriuretic peptide, H2FPEF scores, and echocardiogram reports), and mortality. Analyses were stratified by ethnicity and adjusted for socioeconomic status.
UNASSIGNED: Our cohort consisted of 1,261 (64%) White, 578 (29%) Black, and 134 (7%) Asian patients meeting the European Society of Cardiology HFpEF diagnostic criteria. Compared to White patients, Black patients were younger at diagnosis and more likely to have metabolic comorbidities (obesity, diabetes, and hypertension) but less likely to have atrial fibrillation (30% vs 13%; P < 0.001). Black patients had lower N-terminal pro-B-type natriuretic peptide levels and a lower frequency of H2FPEF scores ≥6, indicative of likely HFpEF (26% vs 44%; P < 0.0001).
UNASSIGNED: Leveraging an NLP-based artificial intelligence approach to quantify health inequities in HFpEF diagnosis, we discovered that established markers systematically underdiagnose HFpEF in Black patients, possibly due to differences in the underlying comorbidity patterns. Clinicians should be aware of these limitations and its implications for treatment and trial recruitment.

New forms of interaction made possible by developments in special educational technologies can now help students with dyscalculia. Artificial intelligence (AI) has emerged as a promising tool in recent decades, particularly between 2001 and 2010, offering avenues to enhance the quality of education for individuals with dyscalculia. Therefore, the implementation of AI becomes crucial in addressing the needs of students with dyscalculia. Content analysis techniques were used to examine the literature covering the influence of AI on dyscalculia and its potential to assist instructors in promoting education for individuals with dyscalculia. The study sought to create a foundation for a more inclusive dyscalculia education in the future through in-depth studies. AI integration has had a big impact on educational institutions as well as people who struggle with dyscalculia. This paper highlights the importance of AI in improving the educational outcomes of students affected by dyscalculia.

UNASSIGNED: Joint bleeding can lead to synovitis and arthropathy in people with hemophilia, reducing quality of life. Although early diagnosis is associated with improved therapeutic outcomes, diagnostic ultrasonography requires specialist experience. Artificial intelligence (AI) algorithms may support ultrasonography diagnoses.
UNASSIGNED: This study will research, develop, and evaluate the diagnostic precision of an AI algorithm for detecting the presence or absence of hemarthrosis and synovitis in people with hemophilia.
UNASSIGNED: Elbow, knee, and ankle ultrasound images were obtained from people with hemophilia from January 2010 to March 2022. The images were used to train and test the AI models to estimate the presence/absence of hemarthrosis and synovitis. The primary endpoint was the area under the curve for the diagnostic precision to diagnose hemarthrosis and synovitis. Other endpoints were the rate of accuracy, precision, sensitivity, and specificity.
UNASSIGNED: Out of 5649 images collected, 3435 were used for analysis. The area under the curve for hemarthrosis detection for the elbow, knee, and ankle joints was ≥0.87 and for synovitis, it was ≥0.90. The accuracy and precision for hemarthrosis detection were ≥0.74 and ≥0.67, respectively, and those for synovitis were ≥0.83 and ≥0.74, respectively. Analysis across people with hemophilia aged 10 to 60 years showed consistent results.
UNASSIGNED: AI models have the potential to aid diagnosis and enable earlier therapeutic interventions, helping people with hemophilia achieve healthy and active lives. Although AI models show potential in diagnosis, evidence is unclear on required control for abnormal findings. Long-term observation is crucial for assessing impact on joint health.

UNASSIGNED: Intrastromal corneal ring segments are commonly implanted in the corneas of eyes with mild-to-moderate keratoconus; however, changes in corneal densitometry (CD) after implantation are a matter of debate in the current literature. We evaluated the changes in CD 1 and 3 months after femtosecond laser-assisted Keraring implantation.
UNASSIGNED: This retrospective, non-comparative, multicenter, case series study included patients with keratoconus who underwent femtosecond laser-assisted implantation of double segments with 90° and 160° arc lengths or two 160° arc length Keraring segments. Demographic and baseline clinical ophthalmic data were recorded. Corneal topography and tomography data acquired using a Pentacam HR Scheimpflug tomography system (Pentacam High Resolution; Oculus, Wetzlar, Germany) with a best-fit sphere were used as a reference surface. Using the Pentacam HR, CD measurements were acquired over a corneal area of 12 mm in total and at four concentric zones (0-2, 2-6, 6-10, and 10-12 mm) of three corneal stromal depths: 120 μm of the anterior corneal stromal layer, 60 μm of the posterior corneal stromal layer, and the central layer of stroma lying between these two layers.
UNASSIGNED: We included 40 eyes of 40 patients, including 8 (20%) male and 32 (80%) female individuals, with a mean (standard deviation) age of 21.0 (6.4) years. We observed a significant improvement in the topographic values of steep keratometry (K), flat K, maximum K, and corneal astigmatism (all P < 0.05), but not in the mean K, thinnest corneal pachymetry, corneal thickness at the apex, back elevation, or front elevation (all P > 0.05). The mean total anterior, central, and posterior CD differed significantly among the time points, with a significant increase from the preoperative to the 1-month and 3-month postoperative visits (all P < 0.05) and no difference between those of the 1-month and 3-month postoperative visits (all P > 0.05). The mean CD for the anterior layer in the central, paracentral, and mid-peripheral zones, and the central layer in all four zones, differed significantly among time points, with a significant increase from the preoperative to the 1-month and 3-month postoperative visits (all P < 0.05), which remained unchanged from the 1-month to the 3-month postoperative visit (all P < 0.05), except for the central 2-6-mm zone, which decreased significantly from the 1-month to the 3-month postoperative visit (P < 0.001). The CD of the central 10-12-mm zone did not differ significantly in each pairwise comparison (all P > 0.05). In contrast, CD for the posterior layer in the paracentral zone decreased significantly from the preoperative to the 1-month and 3-month postoperative visits but increased, to a lesser extent, from the 1-month to the 3-month postoperative visit (all P < 0.05).
UNASSIGNED: Femtosecond laser-assisted Keraring implantation significantly changes CD, with improvement in most topography parameters. Further longitudinal studies with larger sample sizes are required to verify these preliminary findings.

OBJECTIVE: To assess the diagnostic accuracy of ChatGPT-4V in interpreting a set of four chest CT slices for each case of COVID-19, non-small cell lung cancer (NSCLC), and control cases, thereby evaluating its potential as an AI tool in radiological diagnostics.
METHODS: In this retrospective study, 60 CT scans from The Cancer Imaging Archive, covering COVID-19, NSCLC, and control cases were analyzed using ChatGPT-4V. A radiologist selected four CT slices from each scan for evaluation. ChatGPT-4V\'s interpretations were compared against the gold standard diagnoses and assessed by two radiologists. Statistical analyses focused on accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), along with an examination of the impact of pathology location and lobe involvement.
RESULTS: ChatGPT-4V showed an overall diagnostic accuracy of 56.76%. For NSCLC, sensitivity was 27.27% and specificity was 60.47%. In COVID-19 detection, sensitivity was 13.64% and specificity of 64.29%. For control cases, the sensitivity was 31.82%, with a specificity of 95.24%. The highest sensitivity (83.33%) was observed in cases involving all lung lobes. The chi-squared statistical analysis indicated significant differences in Sensitivity across categories and in relation to the location and lobar involvement of pathologies.
CONCLUSIONS: ChatGPT-4V demonstrated variable diagnostic performance in chest CT interpretation, with notable proficiency in specific scenarios. This underscores the challenges of cross-modal AI models like ChatGPT-4V in radiology, pointing toward significant areas for improvement to ensure dependability. The study emphasizes the importance of enhancing these models for broader, more reliable medical use.

BACKGROUND: The aim of this study was to describe the proficiency of ChatGPT (GPT-4) on certification style exams from the Canadian Association of Medical Radiation Technologists (CAMRT), and describe its performance across multiple exam attempts.
METHODS: ChatGPT was prompted with questions from CAMRT practice exams in the disciplines of radiological technology, magnetic resonance (MRI), nuclear medicine and radiation therapy (87-98 questions each). ChatGPT attempted each exam five times. Exam performance was evaluated using descriptive statistics, stratified by discipline and question type (knowledge, application, critical thinking). Light\'s Kappa was used to assess agreement in answers across attempts.
RESULTS: Using a passing grade of 65 %, ChatGPT passed the radiological technology exam only once (20 %), MRI all five times (100 %), nuclear medicine three times (60 %), and radiation therapy all five times (100 %). ChatGPT\'s performance was best on knowledge questions across all disciplines except radiation therapy. It performed worst on critical thinking questions. Agreement in ChatGPT\'s responses across attempts was substantial within the disciplines of radiological technology, MRI, and nuclear medicine, and almost perfect for radiation therapy.
CONCLUSIONS: ChatGPT (GPT-4) was able to pass certification style exams for radiation technologists and therapists, but its performance varied between disciplines. The algorithm demonstrated substantial to almost perfect agreement in the responses it provided across multiple exam attempts. Future research evaluating ChatGPT\'s performance on standardized tests should consider using repeated measures.

Artificial intelligence (AI) technologies are poised to become an increasingly important part of education in the anatomical sciences. OpenAI has also introduced generative pretrained transformers (GPTs), which are customizable versions of the standard ChatGPT application. There is little research that has explored the potential of GPTs to serve as intelligent tutoring systems for learning the anatomical sciences. The objective of this study was to describe the design and explore the performance of AnatomyGPT, a customized artificial intelligence application intended for anatomical sciences education. The AnatomyGPT application was configured with GPT Builder by uploading open-source textbooks as knowledge sources and by providing pedagogical instructions for how to interact with users. The performance of AnatomyGPT was compared with ChatGPT by evaluating the responses of both applications to prompts of the National Board of Medical Examiners (NBME) sample items with respect to accuracy, rationales, and citations. AnatomyGPT achieved high scores on the NBME sample items for Gross Anatomy, Embryology, Histology, and Neuroscience and scored comparably to ChatGPT. In addition, AnatomyGPT provided several citations in the responses that it generated, while ChatGPT provided none. Both GPTs provided rationales for all sample items. The customized AnatomyGPT application demonstrated preliminary potential as an intelligent tutoring system by generating responses with increased citations as compared with the standard ChatGPT application. The findings of this study suggest that instructors and students may wish to create their own custom GPTs for teaching and learning anatomy. Future research is needed to further develop and characterize the potential of GPTs for anatomy education.