This AI Paper Survey Addresses the Role of Large Language Models (LLMs) in Medicine: Their Challenges, Principles And Applications

Natural Language Processing (NLP) has come a long way in the last few months, especially with the introduction of Large Language Models (LLMs). Models like GPT, PaLM, LLaMA, etc., have gained a lot of popularity due to their capability to execute several NLP tasks like text generation, text summarization, and question answering. Researchers have been constantly trying to use the power of LLMs in the medical field.

Medical LLMs, including ChatDoctor, MedAlpaca, PMC-LLaMA, BenTsao, MedPaLM, and Clinical Camel, are used to improve patient care and support medical practitioners. Though current medical LLMs have shown good results, some challenges still need to be addressed. Many models overlook the practical value of biomedical NLP tasks like dialogue and question-answering in clinical settings. The potential of medical LLMs in clinical contexts like Electronic Health Records (EHRs), discharge summary production, health education, and care planning has been the subject of recent efforts; however, these models frequently lack a common evaluation dataset.

Another drawback is that the majority of medical LLMs currently in use assess candidates exclusively on their ability to respond to medical questions, ignoring other crucial biomedical tasks like information retrieval, text production, relation extraction, and text summarization. To overcome these issues, a team of researchers has conducted a study while exploring different facets of medical LLMs by answering five main questions, which are as follows.

  1. Creating Medical LLMs: The first question aims to investigate the approaches and factors that go into creating medical LLMs. This involves comprehending the underlying ideas behind the creation of these models, as well as their structures, training sets, and other pertinent elements.
  1. Evaluation of Medical LLMs’ Downstream Performances: The second question centers on assessing the medical LLMs’ practical results or performances. This includes evaluating these models’ performance in real-world situations, especially when it comes to clinical medicine-related tasks. 
  1. Use of Medical LLMs in Actual Clinical Practice: The third query explores how medical LLMs are actually used in clinical settings. This involves investigating how these models might be included in healthcare practitioners’ regular workflows to improve communication, decision-making, and patient care in general.
  1. Problems Resulting from the Application of Medical LLMs: The fourth question recognizes that there are obstacles associated with using medical LLMs, just like with any other technology. In order to responsibly and successfully implement these models in a healthcare setting, a number of hurdles may need to be addressed, including ethical issues, potential biases in the models, and interpretability problems. 
  1. Building and Applying Medical LLMs Successfully: The last question asks about the future to shed light on improving the design and application of medical LLMs in order to guarantee that medical LLMs continue to develop as useful instruments in the medical industry.

In conclusion, this survey extensively analyzes LLMs in the medical field. It summarises assessments obtained from 10 different biomedical activities and provides a detailed overview of their applications. By addressing key issues, the study seeks to offer a comprehensive knowledge of medical LLMs, encouraging more in-depth analysis, teamwork, and quicker advancement in the medical AI space.


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Tanya Malhotra is a final year undergrad from the University of Petroleum & Energy Studies, Dehradun, pursuing BTech in Computer Science Engineering with a specialization in Artificial Intelligence and Machine Learning.
She is a Data Science enthusiast with good analytical and critical thinking, along with an ardent interest in acquiring new skills, leading groups, and managing work in an organized manner.

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