Fine-Tuning LLMs for Specialized Use Cases

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

Large language models in oncology: a review

BMJ Oncology, Journal Year: 2025, Volume and Issue: 4(1), P. e000759 - e000759

Published: May 1, 2025

Large language models (LLMs) have demonstrated emergent human-like capabilities in natural processing, leading to enthusiasm about their integration healthcare environments. In oncology, where synthesising complex, multimodal data is essential, LLMs offer a promising avenue for supporting clinical decision-making, enhancing patient care, and accelerating research. This narrative review aims highlight the current state of medicine; applications oncology clinicians, patients, translational research; future research directions. Clinician-facing enable decision support automated extraction from electronic health records literature inform decision-making. Patient-facing potential disseminating accessible cancer information psychosocial support. However, face limitations that must be addressed before adoption, including risks hallucinations, poor generalisation, ethical concerns, scope integration. We propose incorporation within compound artificial intelligence systems facilitate adoption efficiency oncology. serves as non-technical primer clinicians understand, evaluate, participate active users who can design iterative improvement LLM technologies deployed settings. While are not intended replace oncologists, they serve powerful tools augment expertise patient-centred reinforcing role valuable adjunct evolving landscape

Language: Английский