Optimized Deep Learning for Multi-Class Retinal Disease Classification Using ResNet-101 DOI

Kunda Suresh Babu,

G. Saranya,

Kattiri. Santhoshkumar

et al.

Published: Nov. 14, 2024

Language: Английский

Artificial Intelligence-Large Language Models (AI-LLMs) for Reliable and Accurate Cardiotocography (CTG) Interpretation in Obstetric Practice DOI Creative Commons
Khanisyah Erza Gumilar, Manggala Pasca Wardhana, Muhammad Ilham Aldika Akbar

et al.

Computational and Structural Biotechnology Journal, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

Citations

0
Validating large language models against manual information extraction from case reports of drug-induced parkinsonism in patients with schizophrenia spectrum and mood disorders: a proof of concept study DOI Creative Commons
Sebastian Volkmer,

Abbe R. Gluck,

Andreas Meyer‐Lindenberg

et al.

Schizophrenia, Journal Year: 2025, Volume and Issue: 11(1)

Published: March 20, 2025

Abstract In this proof of concept study, we demonstrated how Large Language Models (LLMs) can automate the conversion unstructured case reports into clinical ratings. By leveraging instructions from a standardized rating scale and evaluating LLM’s confidence in its outputs, aimed to refine prompting strategies enhance reproducibility. Using strategy drug-induced Parkinsonism, showed that LLM-extracted data closely align with rater manual extraction, achieving an accuracy 90%.

Language: Английский

Citations

0
Metamorphic Testing for robustness and fairness evaluation of LLM-based automated ICD coding applications DOI

Guna Sekaran Jaganathan,

Indika Kahanda, Upulee Kanewala

et al.

Smart Health, Journal Year: 2025, Volume and Issue: unknown, P. 100564 - 100564

Published: April 1, 2025

Language: Английский

Citations

0
Visible light human activity recognition driven by generative language model DOI
Yanbing Yang, Ziwei Liu, Yongkun Chen

et al.

Information Fusion, Journal Year: 2025, Volume and Issue: unknown, P. 103159 - 103159

Published: April 1, 2025

Language: Английский

Citations

0
Application of large language models in medicine DOI
Fenglin Liu, Hongjian Zhou, 博司 熊谷

et al.

Nature Reviews Bioengineering, Journal Year: 2025, Volume and Issue: unknown

Published: April 7, 2025

Language: Английский

Citations

0
An Evaluation of Large Language Models for Supplementing a Food Extrusion Dataset DOI Creative Commons
Necva Bölücü, Jordan Pennells,

Huichen Yang

et al.

Foods, Journal Year: 2025, Volume and Issue: 14(8), P. 1355 - 1355

Published: April 15, 2025

Food extrusion is a widely used processing technique that transforms raw ingredients into structured food products—foods with well-defined textures, shapes, and functionalities—through mechanical shear thermal energy. Despite its broad industrial application, the absence of standardised, dataset capturing research parameters has hindered synthesis, product development, process optimisation. To address this gap, we introduce manually curated literature publication details, types, parameters, formulation data, experimental variables, characterisation metrics, study-level insights. However, while datasets are typically high quality, their scope limited by time resource constraints. We propose method to supplement using large language models (LLMs) evaluate accuracy LLMs in extracting data from scientific literature. Our findings demonstrate can effectively extract information. some challenges, such as hallucination missing contextual remain, suggesting human effort be spent on validating resulting data. This still represents significant savings validation less time-consuming task than extraction. argue thus represent viable tool providing supplementary datasets, leverage existing efforts creation improve quality.

Language: Английский

Citations

0
Large Language Models in Genomics—A Perspective on Personalized Medicine DOI Creative Commons
Shahid Ali, Yazdan Ahmad Qadri, Khurshid Ahmad

et al.

Bioengineering, Journal Year: 2025, Volume and Issue: 12(5), P. 440 - 440

Published: April 23, 2025

Integrating artificial intelligence (AI), particularly large language models (LLMs), into the healthcare industry is revolutionizing field of medicine. LLMs possess capability to analyze scientific literature and genomic data by comprehending producing human-like text. This enhances accuracy, precision, efficiency extensive analyses through contextualization. have made significant advancements in their ability understand complex genetic terminology accurately predict medical outcomes. These capabilities allow for a more thorough understanding influences on health issues creation effective therapies. review emphasizes LLMs’ impact healthcare, evaluates triumphs limitations processing, makes recommendations addressing these order enhance system. It explores latest analysis, focusing enhancing disease diagnosis treatment accuracy taking account an individual’s composition. also anticipates future which AI-driven analysis commonplace clinical practice, suggesting potential research areas. To effectively leverage personalized medicine, it vital actively support innovation across multiple sectors, ensuring that AI developments directly contribute solutions tailored individual patients.

Language: Английский

Citations

0
Comparing Diagnostic Accuracy of Clinical Professionals and Large Language Models: Systematic Review and Meta-Analysis DOI Creative Commons

Guxue Shan,

Xiaonan Chen,

Chen Wang

et al.

JMIR Medical Informatics, Journal Year: 2025, Volume and Issue: 13, P. e64963 - e64963

Published: April 25, 2025

Abstract Background With the rapid development of artificial intelligence (AI) technology, especially generative AI, large language models (LLMs) have shown great potential in medical field. Through massive data training, it can understand complex texts and quickly analyze records provide health counseling diagnostic advice directly, rare diseases. However, no study has yet compared extensively discussed performance LLMs with that physicians. Objective This systematically reviewed accuracy clinical diagnosis provided reference for further application. Methods We conducted searches CNKI (China National Knowledge Infrastructure), VIP Database, SinoMed, PubMed, Web Science, Embase, CINAHL (Cumulative Index to Nursing Allied Health Literature) from January 1, 2017, present. A total 2 reviewers independently screened literature extracted relevant information. The risk bias was assessed using Prediction Model Risk Bias Assessment Tool (PROBAST), which evaluates both applicability included studies. Results 30 studies involving 19 a 4762 cases were included. quality assessment indicated high majority studies, primary cause is known case diagnosis. For optimal model, ranged 25% 97.8%, while triage 66.5% 98%. Conclusions demonstrated considerable capabilities significant application across various cases. Although their still falls short professionals, if used cautiously, they become one best intelligent assistants field human care.

Language: Английский

Citations

0
Optimized Deep Learning for Multi-Class Retinal Disease Classification Using ResNet-101 DOI

Kunda Suresh Babu,

G. Saranya,

Kattiri. Santhoshkumar

et al.

Published: Nov. 14, 2024

Language: Английский

Citations

0