Study Purpose: This paper aims to explore the effectiveness of ChatGPT in facilitating learning for medical students with special educational needs (SEN) while acknowledging and addressing the challenges that SEN students may encounter in utilizing this technology.
Methods: This cross-sectional survey study assessed ChatGPT's efficacy in supporting medical students with SEN across three Saudi Arabian universities. Utilizing purposive and convenience sampling, a questionnaire was administered to 283 SEN students. Statistical analyses, including -tests and ANOVA, were conducted to evaluate perceptions of ChatGPT's effectiveness, considering demographic factors and impairment types.
Results: Notable differences were observed in perceptions of ChatGPT's effectiveness by impairment type and education level. Statistically significant differences were observed among the participants with different types of impairments in relation to flexibility in communication ( = .01), scaffolding and guided practice ( = .0435), immediate feedback and reinforcement ( = .0334), visual and audio support ( = .0244), and simplified learning ( = .002) factors. For instance, individuals with communication and interaction impairments rated ChatGPT's support significantly higher for simplified learning ( = 4.39, = .002) and visual/audio support ( = 4.08, = .024) compared to other impairments. Education level significantly influenced perceptions across all support factors ( < .05), with diploma holders consistently rating ChatGPT more favorably.
Conclusion: Although by providing personalized, simplified, and scaffolded learning experiences, along with social and emotional support, ChatGPT demonstrates promising potential in enhancing learning of SEN students; it does not prove to be effective across all types of impairments.
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http://dx.doi.org/10.1177/02601060241307770 | DOI Listing |
J Med Internet Res
January 2025
Department of Industrial and Systems Engineering, The University of Florida, GAINESVILLE, FL, United States.
Background: The implementation of large language models (LLMs), such as BART (Bidirectional and Auto-Regressive Transformers) and GPT-4, has revolutionized the extraction of insights from unstructured text. These advancements have expanded into health care, allowing analysis of social media for public health insights. However, the detection of drug discontinuation events (DDEs) remains underexplored.
View Article and Find Full Text PDFBMC Pregnancy Childbirth
January 2025
Department of Obstetrics and Gynecology, Division of Maternal-Fetal Medicine, University of Utah Health, 30 N. Mario Capecchi Dr., Level 5 South, Salt Lake City, UT, 84132, USA.
Background: Fetal growth restriction (FGR) is a leading risk factor for stillbirth, yet the diagnosis of FGR confers considerable prognostic uncertainty, as most infants with FGR do not experience any morbidity. Our objective was to use data from a large, deeply phenotyped observational obstetric cohort to develop a probabilistic graphical model (PGM), a type of "explainable artificial intelligence (AI)", as a potential framework to better understand how interrelated variables contribute to perinatal morbidity risk in FGR.
Methods: Using data from 9,558 pregnancies delivered at ≥ 20 weeks with available outcome data, we derived and validated a PGM using randomly selected sub-cohorts of 80% (n = 7645) and 20% (n = 1,912), respectively, to discriminate cases of FGR resulting in composite perinatal morbidity from those that did not.
Nat Ecol Evol
January 2025
Center for Ecosystem Sentinels, Department of Biology, University of Washington, Seattle, WA, USA.
The emergence of generative artificial intelligence (AI) models specializing in the generation of new data with the statistical patterns and properties of the data upon which the models were trained has profoundly influenced a range of academic disciplines, industry and public discourse. Combined with the vast amounts of diverse data now available to ecologists, from genetic sequences to remotely sensed animal tracks, generative AI presents enormous potential applications within ecology. Here we draw upon a range of fields to discuss unique potential applications in which generative AI could accelerate the field of ecology, including augmenting data-scarce datasets, extending observations of ecological patterns and increasing the accessibility of ecological data.
View Article and Find Full Text PDFBackground: Multidisciplinary tumor boards (MTBs) have been established in most countries to allow experts collaboratively determine the best treatment decisions for cancer patients. However, MTBs often face challenges such as case overload, which can compromise MTB decision quality. Clinical decision support systems (CDSSs) have been introduced to assist clinicians in this process.
View Article and Find Full Text PDFEur J Nucl Med Mol Imaging
January 2025
Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Spitalgasse 23, Vienna, 1090, Austria.
Purpose: Advancements of deep learning in medical imaging are often constrained by the limited availability of large, annotated datasets, resulting in underperforming models when deployed under real-world conditions. This study investigated a generative artificial intelligence (AI) approach to create synthetic medical images taking the example of bone scintigraphy scans, to increase the data diversity of small-scale datasets for more effective model training and improved generalization.
Methods: We trained a generative model on Tc-bone scintigraphy scans from 9,170 patients in one center to generate high-quality and fully anonymized annotated scans of patients representing two distinct disease patterns: abnormal uptake indicative of (i) bone metastases and (ii) cardiac uptake indicative of cardiac amyloidosis.
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