Natural Language Processing (NLP) has come a long way in the past few months, especially with the introduction of Large Language Models (LLMs). Models like GPT, PaLM, LLaMA, etc. have gained great popularity due to their ability to perform many NLP tasks such as text generation, text summarization, and question answering. Researchers have continually tried to use the power of LLMs in the medical field.
Medical LLM degrees, including ChatDoctor, MedAlpaca, PMC-LLaMA, BenTsao, MedPaLM, and Clinical Camel, are used to improve patient care and support medical practitioners. Although the current MSc in Medical Sciences has shown good results, some challenges still need to be addressed. Many models ignore the practical value of biomedical NLP tasks such as dialogue and question answering in clinical settings. The potential of MSc in clinical contexts such as electronic health records (EHRs), discharge summary production, health education, and care planning have been the subject of recent efforts; However, these models often lack a common evaluation data set.
Another drawback is that the majority of currently used MScs evaluate candidates exclusively on their ability to respond to medical questions, ignoring other crucial biomedical tasks such as information retrieval, text production, relationship extraction, and text summarization. To overcome these issues, a team of researchers conducted a study while exploring different aspects of MD by answering five main questions, which are as follows.
- Establishing a Master in Medical Law: The first question aims to examine the methods and factors involved in establishing a Master in Medical Law. This includes understanding the basic ideas behind creating these models, as well as their structures, training sets, and other related elements.
- Evaluating the final performance of Master of Medical Sciences holders: The second question focuses on evaluating the results or practical performance of Master of Medical Law holders. This includes evaluating the performance of these models in real-world situations, especially when it comes to tasks related to clinical medicine.
- Using medical LLMs in actual clinical practice: The third inquiry explores how medical LLMs are actually used in clinical settings. This includes investigating how these models can be incorporated into healthcare practitioners’ regular workflow to improve communication, decision-making and overall patient care.
- Problems resulting from the application of the MSc in Medical Sciences: Question 4 explains that there are obstacles associated with the use of the MSc in Medical Sciences, just as with any other technology. In order to responsibly and successfully implement these models in the healthcare setting, a number of obstacles may need to be addressed, including ethical issues, potential biases in the models, and problems of interpretation.
- Successfully Building and Applying Medical LLMs: The final question asks about the future to highlight the improvement of the design and application of medical LLMs in order to ensure the continued development of medical LLMs as useful tools in the medical industry.
In conclusion, this survey broadly analyzes LLM in the medical field. It summarizes evaluations obtained from 10 different biomedical activities and provides a detailed overview of their applications. By addressing key issues, the study seeks to provide a comprehensive knowledge of LLM, encouraging more in-depth analysis, teamwork, and faster progress in the field of medical AI.
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Tanya Malhotra is a final year undergraduate student from University of Petroleum and Energy Studies, Dehradun, studying B.Tech in Computer Science Engineering with specialization in Artificial Intelligence and Machine Learning.
She is passionate about data science and has good analytical and critical thinking, along with a keen interest in acquiring new skills, leading groups and managing work in an organized manner.
In recent years, large linguistic models (LLMs) have increasingly gained attention in the medical field for their potential to revolutionize various aspects of healthcare, from patient care to research and diagnostics. This AI-based survey aims to explore the role of LLMs in medicine, including the challenges they present, the principles underlying their functioning, and their wide-ranging applications in the healthcare industry. By delving into the capabilities and limitations of LLMs in the medical context, this study seeks to provide valuable insights into the future potential of these advanced linguistic models in advancing the standard of care and medical knowledge.