Heliyon, Год журнала: 2024, Номер 10(24), С. e40964 - e40964
Опубликована: Дек. 1, 2024
Язык: Английский
Contemporary Nurse, Год журнала: 2025, Номер unknown, С. 1 - 6
Опубликована: Янв. 3, 2025
Keywords: artificial intelligencenursing informaticsclinical decision support systemsprecision medicineethics, nursingdiagnostic imaging
Язык: Английский
Процитировано
3
Current Problems in Diagnostic Radiology, Год журнала: 2024, Номер 53(6), С. 728 - 737
Опубликована: Июль 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.
Язык: Английский
Процитировано
14
Diagnostics, Год журнала: 2024, Номер 14(7), С. 773 - 773
Опубликована: Апрель 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.
Язык: Английский
Процитировано
8
Healthcare, Год журнала: 2025, Номер 13(5), С. 556 - 556
Опубликована: Март 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.
Язык: Английский
Процитировано
1
Diagnostics, Год журнала: 2024, Номер 14(12), С. 1265 - 1265
Опубликована: Июнь 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”.
Язык: Английский
Процитировано
6
Radiology, Год журнала: 2024, Номер 313(2)
Опубликована: Ноя. 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.
Язык: Английский
Процитировано
5
Diagnostics, Год журнала: 2024, Номер 14(2), С. 171 - 171
Опубликована: Янв. 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.
Язык: Английский
Процитировано
4
Deleted Journal, Год журнала: 2024, Номер 1(1)
Опубликована: Янв. 1, 2024
Abstract Generative artificial intelligence (AI) has enabled tasks in radiology, including tools for improving image quality. Recently, new hotspots have emerged, such as intra- or inter-modal translation, task-specific synthesis, and text generation. Advances generative AI facilitated the move towards low-dose, cost-effective, high-quality radiological acquisition. Large language models can aid radiologists by generating professional answers facilitating patient-physician communications. However, must be aware of potential inaccuracies generated content should only use after rigorous validation their performance.
Язык: Английский
Процитировано
3
European Radiology, Год журнала: 2024, Номер unknown
Опубликована: Окт. 18, 2024
Abstract Objectives ChatGPT-4 Vision (GPT-4V) is a state-of-the-art multimodal large language model (LLM) that may be queried using images. We aimed to evaluate the tool’s diagnostic performance when autonomously assessing clinical imaging studies. Materials and methods A total of 206 studies (i.e., radiography ( n = 60), CT MRI angiography 26)) with unequivocal findings established reference diagnoses from radiologic practice university hospital were accessed. Readings performed uncontextualized, only image provided, contextualized, additional demographic information. Responses assessed along multiple dimensions analyzed appropriate statistical tests. Results With its pronounced propensity favor context over information, accuracy improved 8.3% (uncontextualized) 29.1% (contextualized, first diagnosis correct) 63.6% correct among differential diagnoses) p ≤ 0.001, Cochran’s Q test). Diagnostic declined by up 30% 20 images re-read after 30 90 days seemed unrelated self-reported confidence (Spearman’s ρ 0.117 0.776)). While described matched suggested in 92.7%, indicating valid reasoning, tool fabricated 258 412 responses misidentified modalities or anatomic regions 65 Conclusion GPT-4V, current form, cannot reliably interpret Its tendency disregard image, fabricate findings, misidentify details, especially without context, misguide healthcare providers put patients at risk. Key Points Question Can Generative Pre-trained Transformer 4 images—with context? Findings GPT-4V poorly, demonstrating rates 8% (uncontextualized), 29% most likely correct), 64% diagnoses). Clinical relevance The utility commercial models, such as limited. Without errors compromise patient safety decision-making. These models must further refined beneficial.
Язык: Английский
Процитировано
3
Japanese Journal of Radiology, Год журнала: 2024, Номер unknown
Опубликована: Ноя. 14, 2024
Язык: Английский
Процитировано
3