Part 1: Understanding the role of Medical Large Language Models in Healthcare

Recently, general large language models (LLMs), like PaLM, LLaMA, GPT-series, and ChatGLM, which have already revolutionized various natural language processing tasks such as text generation, summarization, and question answering, have been adapted to the medical field giving rise to medical LLMs. These adaptations, such as MedPaLM-2 and MedPrompt, built on the foundations of PaLM and GPT-4, have achieved competitive accuracies of 86.5 and 90.2 respectively, closely rivaling human experts' accuracy of 87.0 in the United States Medical Licensing Examination (USMLE). Adding on to this wave, the release of LLama, the open source variant has given rise to various medical LLM like ChatDoctor, MedAlpaca, PMC-LLaMA, BenTsao to name few.

The given graphs depict the present status of Medical Large Language Models (LLMs) in regard to their performance on various tasks within the healthcare sector. The assessments are carried out using a standard dataset. It is evident from the data that the accuracy of most tasks is high and nearing the level of human intervention.

In India, Medical LLMs can play many roles within the current healthcare industry. Broadly, these roles can be categorized into the following buckets:

Analyze vast amounts of healthcare data for insights

Medical Large Language Models can provide valuable insights by analyzing a vast range of healthcare data from various sources, including genetic, lifestyle, and pathophysical data. Their precision in integrating different data sources can aid healthcare professionals in quickly triaging patients. Triage often consumes a significant portion of time, with an average consultation time in India being 9.5 minutes. If a pre-triage team, supported by a medical LLM, is involved, this consultation time can be reduced. This could also decrease the average patient wait time, currently at 50.4 minutes.

Predicting and Diagnosing diseases by recognizing patterns

The process of predicting and diagnosing diseases often involves identifying patterns in data, an extension of analysis. However, the limitation of time might hinder healthcare professionals from asking the right questions necessary for precise diagnosis and prediction, leading to potential inaccuracies. To alleviate this issue, a question-answering system could be implemented for triaging purposes. This would free up doctors to spend more time explaining the patient's condition and necessary treatments, fostering a more empathetic patient-doctor relationship.

Medical Transcription and digitization.

Often, a doctor's prescription is hard to read and only decipherable to those trained to interpret the handwriting. This difficulty arises due to the immense workload doctors handle, negatively affecting their handwriting. However, transcription errors can easily occur when converting these handwritten notes and prescriptions into digital format. The accurate digitalization of records is essential for understanding a patient's history, making precise transcription crucial. These models can also adapt to the unique speech patterns and accents of different practitioners, further improving the quality of transcriptions.

Evidence based suggestion

Often, healthcare professionals don't provide sufficient information when diagnosing a disease. A specific disease can have varied treatments depending on the individual patient. However, this personalized information is not always communicated, as doctors may lack the time to explain their decisions. Medical Large Language Models (LLMs) can be instrumental in this regard by generating evidence-based information. This information can be reviewed by the doctors and subsequently shared with the patients for better understanding.

Medical research and summarization

These models have the capability to sift through thousands of research papers, articles, and case studies in a fraction of the time it would take a human researcher. They can pick out key points, summarise complex information, and present it in a digestible format. This helps in keeping medical professionals up-to-date with the latest advancements and findings in their field. Additionally, they can also identify trends, patterns, and connections across different studies, potentially uncovering new insights that can lead to breakthroughs in medical research.

Enhance telemedicine by aiding virtual consultations

In a country like India, where the doctor-patient ratio is significantly challenged, Medical Large Language Models can play a transformative role in enhancing telemedicine. Supporting virtual consultations, these models can provide substantial relief to the healthcare system by using their extensive knowledge base to understand patient symptoms and medical history, thereby offering a preliminary diagnosis for doctors to consider. This becomes particularly beneficial in rural and remote areas of India, where access to healthcare can be limited and patient loads can be overwhelming. These models have the potential to significantly boost the efficiency and effectiveness of telemedicine in India, helping to bridge the gap between the demand for and supply of healthcare professionals.

In conclusion, Medical Large Language Models hold immense potential to revolutionize India's healthcare sector. From analyzing vast healthcare data for insights, aiding in disease prediction and diagnosis, to enhancing telemedicine, these models present a unique opportunity to address several challenges in the healthcare industry. By leveraging their capabilities, we can not only streamline medical processes but also foster more effective and empathetic patient-doctor relationships. As we continue to innovate and adapt these models for specific healthcare applications, the future of healthcare in India looks promising.

In the next part of this blog series, we will explore technical aspects of Medical LLMs

Reference:

A Survey of Large Language Models in Medicine: Progress, Application, and Challenge, Zhou et al, 2024.

https://economictimes.indiatimes.com/news/india/doctor-population-ratio-in-country-stands-at-1834-mansukh-mandaviya-tells-lok-sabha/articleshow/107561323.cms?from=mdr, Accessed on June 29, 2024