Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 110, P. 108142 - 108142
Published: May 30, 2025
Language: Английский
Journal of Multidisciplinary Healthcare, Journal Year: 2025, Volume and Issue: Volume 18, P. 223 - 238
Published: Jan. 1, 2025
The integration of large language models (LLMs) in healthcare has generated significant interest due to their potential improve diagnostic accuracy, personalization treatment, and patient care efficiency. This study aims conduct a comprehensive bibliometric analysis identify current research trends, main themes future directions regarding applications the sector. A systematic scan publications until 08.05.2024 was carried out from an important database such as Web Science.Using tools VOSviewer CiteSpace, we analyzed data covering publication counts, citation analysis, co-authorship, co- occurrence keywords thematic development map intellectual landscape collaborative networks this field. included more than 500 articles published between 2021 2024. United States, Germany Kingdom were top contributors highlights that neural network imaging, natural processing for clinical documentation, field general internal medicine, radiology, medical informatics, health services, surgery, oncology, ophthalmology, neurology, orthopedics psychiatry have seen growth over past two years. Keyword trend revealed emerging sub-themes research, artificial intelligence, ChatGPT, education, processing, management, virtual reality, chatbot, indicating shift towards addressing broader implications LLM application healthcare. use is expanding with academic interest. not only maps state but also identifies areas require further development. Continued advances are expected significantly impact applications, focus on increasing accuracy through advanced analytics.
Language: Английский
Citations
4
Information Fusion, Journal Year: 2024, Volume and Issue: 114, P. 102697 - 102697
Published: Sept. 16, 2024
Language: Английский
Citations
10
Mayo Clinic Proceedings Digital Health, Journal Year: 2025, Volume and Issue: unknown, P. 100197 - 100197
Published: Jan. 1, 2025
This study aimed to evaluate the quality of evidence for using machine learning models predict inpatient admissions from emergency department triage data, ultimately aiming improve patient flow management. A comprehensive literature search was conducted according PRISMA guidelines across 5 databases, PubMed, Embase, Web Science, Scopus, and CINAHL, on August 1, 2024, English-language studies published between 2014, 2024. yielded 700 articles, which 66 were screened in full, 31 met inclusion exclusion criteria. Model assessed PROBAST appraisal tool a modified TRIPOD+AI framework, alongside reported model performance metrics. Seven demonstrated rigorous methodology promising silico performance, with an area under receiver operating characteristic ranging 0.81 0.93. However, further analysis limited by heterogeneity development unclear-to-high risk bias applicability concerns remaining 24 as evaluated tool. The current demonstrates good degree accuracy predicting admission data alone. Future research should emphasize transparent reporting, temporal validation, concept drift analysis, exploration emerging artificial intelligence techniques, real-world metrics comprehensively assess usefulness these models.
Language: Английский
Citations
2
npj Digital Medicine, Journal Year: 2024, Volume and Issue: 7(1)
Published: Nov. 25, 2024
John J. Hopfield and Geoffrey E. Hinton were awarded the 2024 Nobel Prize in Physics for developing machine learning technology using artificial neural networks. In Chemistry it was to Demis Hassabis M. Jumper an AI algorithm that solved 50-year protein structure prediction challenge. This highlights AI's impact on science, medicine society; however, winners acknowledge ethical aspects of must be considered.
Language: Английский
Citations
7
Lecture notes in computer science, Journal Year: 2025, Volume and Issue: unknown, P. 221 - 232
Published: Jan. 1, 2025
Language: Английский
Citations
1
La radiologia medica, Journal Year: 2024, Volume and Issue: 129(10), P. 1463 - 1467
Published: Aug. 13, 2024
Language: Английский
Citations
6
Image and Vision Computing, Journal Year: 2025, Volume and Issue: unknown, P. 105495 - 105495
Published: March 1, 2025
Language: Английский
Citations
0
Applied Intelligence, Journal Year: 2025, Volume and Issue: 55(7)
Published: April 5, 2025
Language: Английский
Citations
0
Franklin Open, Journal Year: 2025, Volume and Issue: unknown, P. 100262 - 100262
Published: April 1, 2025
Language: Английский
Citations
0
Documenta Ophthalmologica, Journal Year: 2025, Volume and Issue: unknown
Published: April 16, 2025
Abstract Purpose The electroretinogram (ERG) records the functional response of retina. In some neurological conditions, ERG waveform may be altered and could support biomarker discovery. heterogeneous or rare populations, where either large data sets availability a challenge, synthetic signals with Artificial Intelligence (AI) help to mitigate against these factors classification models. Methods This approach was tested using publicly available dataset real ERGs, n = 560 (ASD) 498 (Control) recorded at 9 different flash strengths from 18 ASD (mean age 12.2 ± 2.7 years) 31 Controls 11.8 3.3 that were augmented waveforms, generated through Conditional Generative Adversarial Network. Two deep learning models used classify groups only combined ERGs. One Time Series Transformer (with waveforms in their original form) second Visual model utilizing images wavelets derived Continuous Wavelet Transform Model performance classifying evaluated Balanced Accuracy (BA) as main outcome measure. Results BA improved 0.756 0.879 when ERGs included across all recordings for training Transformer. also achieved best 0.89 single strength 0.95 log cd s m −2 . Conclusions supports application AI improve group recordings.
Language: Английский
Citations
0