Transforming nursing practice through cutting-edge AI in healthcare: Opportunities, challenges, and ethical implications

Contemporary Nurse, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 6

Published: Jan. 3, 2025

Keywords: artificial intelligencenursing informaticsclinical decision support systemsprecision medicineethics, nursingdiagnostic imaging

Language: Английский

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ChatGPT and assistive AI in structured radiology reporting: A systematic review

Current Problems in Diagnostic Radiology, Journal Year: 2024, Volume and Issue: 53(6), P. 728 - 737

Published: July 9, 2024

The rise of transformer-based large language models (LLMs), such as ChatGPT, has captured global attention with recent advancements in artificial intelligence (AI). ChatGPT demonstrates growing potential structured radiology reporting—a field where AI traditionally focused on image analysis. A comprehensive search MEDLINE and Embase was conducted from inception through May 2024, primary studies discussing ChatGPT's role reporting were selected based their content. Of the 268 articles screened, eight ultimately included this review. These explored various applications generating reports unstructured reports, extracting data free text, impressions findings creating imaging data. All demonstrated optimism regarding to aid radiologists, though common critiques privacy concerns, reliability, medical errors, lack medical-specific training. assistive have significant transform reporting, enhancing accuracy standardization while optimizing healthcare resources. Future developments may involve integrating dynamic few-shot prompting, Retrieval Augmented Generation (RAG) into diagnostic workflows. Continued research, development, ethical oversight are crucial fully realize AI's radiology.

Language: Английский

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Artificial Intelligence in Medical Imaging: Analyzing the Performance of ChatGPT and Microsoft Bing in Scoliosis Detection and Cobb Angle Assessment

Diagnostics, Journal Year: 2024, Volume and Issue: 14(7), P. 773 - 773

Published: April 5, 2024

Open-source artificial intelligence models (OSAIM) find free applications in various industries, including information technology and medicine. Their clinical potential, especially supporting diagnosis therapy, is the subject of increasingly intensive research. Due to growing interest (AI) for diagnostic purposes, we conducted a study evaluating capabilities AI models, ChatGPT Microsoft Bing, single-curve scoliosis based on posturographic radiological images. Two independent neurosurgeons assessed degree spinal deformation, selecting 23 cases severe scoliosis. Each image was separately implemented onto each mentioned platforms using set formulated questions, starting from ‘What do you see image?’ ending with request determine Cobb angle. In responses, focused how these identify interpret deformations accurately they recognize direction type as well vertebral rotation. The Intraclass Correlation Coefficient (ICC) ‘two-way’ model used assess consistency angle measurements, its confidence intervals were determined F test. Differences measurements between human assessments analyzed metrics such RMSEA, MSE, MPE, MAE, RMSLE, MAPE, allowing comprehensive assessment performance statistical perspectives. achieved 100% effectiveness detecting X-ray images, while Bing did not detect any However, had limited (43.5%) assessing angles, showing significant inaccuracy discrepancy compared assessments. This also accuracy determining curvature, classifying scoliosis, Overall, although demonstrated potential abilities angles other parameters inconsistent expert These results underscore need improvement algorithms, broader training diverse images advanced processing techniques, before can be considered auxiliary diagnosing by specialists.

Language: Английский

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Advancements in Artificial Intelligence for Medical Computer-Aided Diagnosis

Diagnostics, Journal Year: 2024, Volume and Issue: 14(12), P. 1265 - 1265

Published: June 15, 2024

Rapid advancements in artificial intelligence (AI) and machine learning (ML) are currently transforming the field of diagnostics, enabling unprecedented accuracy efficiency disease detection, classification, treatment planning. This Special Issue, entitled “Artificial Intelligence Advances for Medical Computer-Aided Diagnosis”, presents a curated collection cutting-edge research that explores integration AI ML technologies into various diagnostic modalities. The contributions presented here highlight innovative algorithms, models, applications pave way improved capabilities across range medical fields, including radiology, pathology, genomics, personalized medicine. By showcasing both theoretical practical implementations, this Issue aims to provide comprehensive overview current trends future directions AI-driven fostering further collaboration dynamic impactful area healthcare. We have published total 12 articles all collected between March 2023 December 2023, comprising 1 Editorial cover letter, 9 regular articles, review article, article categorized as “other”.

Language: Английский

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ChatGPT’s Accuracy on Magnetic Resonance Imaging Basics: Characteristics and Limitations Depending on the Question Type

Diagnostics, Journal Year: 2024, Volume and Issue: 14(2), P. 171 - 171

Published: Jan. 12, 2024

Our study aimed to assess the accuracy and limitations of ChatGPT in domain MRI, focused on evaluating ChatGPT's performance answering simple knowledge questions specialized multiple-choice related MRI. A two-step approach was used evaluate ChatGPT. In first step, 50 MRI-related were asked, answers categorized as correct, partially or incorrect by independent researchers. second 75 covering various MRI topics posed, similarly categorized. The utilized Cohen's kappa coefficient for assessing interobserver agreement. demonstrated high straightforward questions, with over 85% classified correct. However, its varied significantly across rates ranging from 40% 66.7%, depending topic. This indicated a notable gap ability handle more complex, requiring deeper understanding context. conclusion, this critically evaluates addressing Magnetic Resonance Imaging (MRI), highlighting potential healthcare sector, particularly radiology. findings demonstrate that ChatGPT, while proficient responding exhibits variability accurately answer complex require profound, discrepancy underscores nuanced role AI can play medical education decision-making, necessitating balanced application.

Language: Английский

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Toward Foundation Models in Radiology? Quantitative Assessment of GPT-4V’s Multimodal and Multianatomic Region Capabilities

Radiology, Journal Year: 2024, Volume and Issue: 313(2)

Published: Nov. 1, 2024

OpenAI’s GPT-4V reliably identified the imaging modality and anatomic region but could not safely detect, classify, or rule out abnormalities on single MRI, CT, radiographic images.

Language: Английский

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Integrating AI and Assistive Technologies in Healthcare: Insights from a Narrative Review of Reviews

Healthcare, Journal Year: 2025, Volume and Issue: 13(5), P. 556 - 556

Published: March 4, 2025

The integration of artificial intelligence (AI) into assistive technologies is an emerging field with transformative potential, aimed at enhancing autonomy and quality life for individuals disabilities aging populations. This overview reviews, utilizing a standardized checklist control procedures, examines recent advancements future implications in this domain. search articles the review was finalized by 15 December 2024. Nineteen studies were selected through systematic process identifying prevailing themes, opportunities, challenges, recommendations regarding AI technologies. First, increasingly central to improving mobility, healthcare diagnostics, cognitive support, enabling personalized adaptive solutions users. traditional technologies, such as smart wheelchairs exoskeletons, enhances their performance, creating more intuitive responsive devices. Additionally, inclusion children autism spectrum disorders, promoting social interaction development innovative also identifies significant opportunities challenges. AI-powered offer enormous potential increase independence, reduce reliance on external improve communication disorders. However, challenges personalization, digital literacy among elderly, privacy concerns contexts need be addressed. Notably, itself expanding concept technology, shifting from tools intelligent systems capable learning adapting individual needs. evolution represents fundamental change emphasizing dynamic, over static solutions. Finally, study emphasizes growing economic investment sector, forecasting market growth, AI-driven devices poised transform landscape. Despite high costs regulatory hurdles, innovation affordability remain. underscores importance addressing related standardization, accessibility, ethical considerations ensure successful fostering greater inclusivity improved users globally.

Language: Английский

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Decoding Wisdom: Evaluating ChatGPT's Accuracy and Reproducibility in Analyzing Orthopantomographic Images for Third Molar Assessment

Computational and Structural Biotechnology Journal, Journal Year: 2025, Volume and Issue: 28, P. 141 - 147

Published: Jan. 1, 2025

Language: Английский

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Comparative Evaluation of Large Language and Multimodal Models in Detecting Spinal Stabilization Systems on X-Ray Images

Journal of Clinical Medicine, Journal Year: 2025, Volume and Issue: 14(10), P. 3282 - 3282

Published: May 8, 2025

Background/Objectives: Open-source AI models are increasingly applied in medical imaging, yet their effectiveness detecting and classifying spinal stabilization systems remains underexplored. This study compares ChatGPT-4o (a large language model) BiomedCLIP multimodal analysis of posturographic X-ray images (AP projection) to assess accuracy identifying the presence, type (growing vs. non-growing), specific system (MCGR PSF). Methods: A dataset 270 (93 without stabilization, 80 with MCGR, 97 PSF) was analyzed manually by neurosurgeons evaluated using a three-stage AI-based questioning approach. Performance assessed via classification accuracy, Gwet’s Agreement Coefficient (AC1) for inter-rater reliability, two-tailed z-test statistical significance (p < 0.05). Results: The results indicate that GPT-4o demonstrates high systems, achieving near-perfect recognition (97–100%) presence or absence stabilization. However, its consistency is reduced when distinguishing complex growing-rod (MCGR) configurations, agreement scores dropping significantly (AC1 = 0.32–0.50). In contrast, displays greater response 1.00) but struggles detailed classification, particularly recognizing PSF (11% accuracy) MCGR (4.16% accuracy). Sensitivity revealed GPT-4o’s superior stability hierarchical tasks, while excelled binary detection showed performance deterioration as complexity increased. Conclusions: These findings highlight robustness clinical AI-assisted diagnostics, differentiation whereas BiomedCLIP’s precision may require further optimization enhance applicability radiographic evaluations.

Language: Английский

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Large Language Models in radiology : A technical and clinical perspective

Published: May 1, 2025

Language: Английский

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