Clinical Applications and Limitations of Large Language Models in Nephrology: A Systematic Review DOI Creative Commons

Zsuzsa Unger,

Shelly Soffer, Orly Efros

et al.

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 1, 2024

Abstract Background Large Language Models (LLMs) are emerging as promising tools in healthcare. This systematic review examines LLMs’ potential applications nephrology, highlighting their benefits and limitations. Methods We conducted a literature search PubMed Web of Science, selecting studies based on Preferred Reporting Items for Systematic Reviews Meta-Analyses (PRISMA) guidelines. The focuses the latest advancements LLMs nephrology from 2020 to 2024. PROSPERO registration number: CRD42024550169. Results Fourteen met inclusion criteria were categorized into five key areas nephrology: Streamlining workflow, disease prediction prognosis, laboratory data interpretation management, renal dietary patient education. showed high performance various clinical tasks, including managing continuous replacement therapy (CRRT) alarms (GPT-4 accuracy 90-94%) reducing intensive care unit (ICU) alarm fatigue, predicting chronic kidney diseases (CKD) progression (improved positive predictive value 6.7% 20.9%). In education, GPT-4 excelled at simplifying medical information by readability complexity, accurately translating transplant resources. Gemini provided most accurate responses frequently asked questions (FAQs) about CKD. Conclusions While incorporation shows promise across levels care, broad implementation is still premature. Further research required validate these terms accuracy, rare critical conditions, real-world performance.

Language: Английский

Fine-Tuning LLMs for Specialized Use Cases DOI Creative Commons
D. M. Anisuzzaman, Jeffrey G. Malins, Paul A. Friedman

et al.

Mayo Clinic Proceedings Digital Health, Journal Year: 2024, Volume and Issue: 3(1), P. 100184 - 100184

Published: Nov. 29, 2024

Large language models (LLMs) are a type of artificial intelligence, which operate by predicting and assembling sequences words that statistically likely to follow from given text input. With this basic ability, LLMs able answer complex questions extremely instructions. Products created using such as ChatGPT OpenAI Claude Anthropic have huge amount traction user engagements revolutionized the way we interact with technology, bringing new dimension human-computer interaction. Fine-tuning is process in pretrained model, an LLM, further trained on custom data set adapt it for specialized tasks or domains. In review, outline some major methodologic approaches techniques can be used fine-tune use cases enumerate general steps required carrying out LLM fine-tuning. We then illustrate few these describing several specific fine-tuning across medical subspecialties. Finally, close consideration benefits limitations associated cases, emphasis concerns field medicine.

Language: Английский

Citations

9
Exploring the efficacy and potential of large language models for depression: A systematic review DOI
Mahmud Omar, Inbar Levkovich

Journal of Affective Disorders, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 1, 2024

Language: Английский

Citations

8
What is the role of large language models in the management of urolithiasis?: a review DOI
Tunahan Ateş, Nezih Tamkaç,

Ibrahim Halil Sukur

et al.

Urolithiasis, Journal Year: 2025, Volume and Issue: 53(1)

Published: May 15, 2025

Language: Английский

Citations

0
Clinical Applications and Limitations of Large Language Models in Nephrology: A Systematic Review DOI Creative Commons

Zsuzsa Unger,

Shelly Soffer, Orly Efros

et al.

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 1, 2024

Abstract Background Large Language Models (LLMs) are emerging as promising tools in healthcare. This systematic review examines LLMs’ potential applications nephrology, highlighting their benefits and limitations. Methods We conducted a literature search PubMed Web of Science, selecting studies based on Preferred Reporting Items for Systematic Reviews Meta-Analyses (PRISMA) guidelines. The focuses the latest advancements LLMs nephrology from 2020 to 2024. PROSPERO registration number: CRD42024550169. Results Fourteen met inclusion criteria were categorized into five key areas nephrology: Streamlining workflow, disease prediction prognosis, laboratory data interpretation management, renal dietary patient education. showed high performance various clinical tasks, including managing continuous replacement therapy (CRRT) alarms (GPT-4 accuracy 90-94%) reducing intensive care unit (ICU) alarm fatigue, predicting chronic kidney diseases (CKD) progression (improved positive predictive value 6.7% 20.9%). In education, GPT-4 excelled at simplifying medical information by readability complexity, accurately translating transplant resources. Gemini provided most accurate responses frequently asked questions (FAQs) about CKD. Conclusions While incorporation shows promise across levels care, broad implementation is still premature. Further research required validate these terms accuracy, rare critical conditions, real-world performance.

Language: Английский

Citations

0