LLMs in Healthcare

Call for Book Chapters

Large Language Models in Healthcare: Opportunities, Applications, and Challenges

Background:

With the recent developments in large language models (LLMs), we are witnessing a paradigm shift in medical artificial intelligence. LLMs demonstrate potential to help understand clinical language, elevate healthcare quality, accelerate diagnosis and prognosis processes, and enhance decision-making. Furthermore, LLMs offer a capacity to cope with the ever-expanding medical and healthcare knowledge and rapidly rising electronic health records and medical data of patients, that may otherwise leave healthcare professionals struggling with information overload. LLMs offer automation of mining vital information, extract knowledge for analysis of medical data, thus, opening up new dimensions in the healthcare domain. New and existing applications are being explored ranging from summarization of clinical notes, medical question/answering (such as Med-PaLM and Med-PALM2, BioMistral) understanding unstructured notes and knowledge extraction (BioGPT), medical literature analysis, radiology report generation and medical image understanding (Radiology-llama2 , ChatCAD), drug discovery, summary completion, assessment and discharge report completion, medical education and training, medical technology user guidance. Likewise, the advent of new LLMs use cases also give rise of new challenges such as hardware resource constraints and achieving generalization of LLMs across data of different cohorts. More importantly, there are important ethical challenges that one needs to understand and address such as lack of empathy in LLMs-based technology, bias in training data of LLMs, privacy concerns around the use of patients data in LLMs, human feelings and trust when interacting with LLLs-based healthcare tools and accepting LLMs-driven decision in healthcare, and hallucinations. In summary, LLMs have some limitations which need to be considered when using in healthcare. Additionally, there is a need to explore the responsible use of LLMs in healthcare.

We invite book chapters for our upcoming book on Large Language Models in Healthcare: Opportunities, Applications, and Challenges, to be published by Taylor and Francis Group/CRC Press. We invite book chapters covering one or more of the following objectives.

  • Recent applications and use cases of LLMs in healthcare data.
  • Analysis of large-scale healthcare data using LLMs.
  • Automation and decision-making capabilities of LLMs in healthcare applications such as diagnosis and prognosis.
  • Challenges in training, adoption, and fine-tuning of LLMs in healthcare data, ranging from training/implementation phase to deployment phase.
  • Trust and ethics in LLMs for healthcare applications.
  • Regulatory efforts around the globe on using LLMs in healthcare.