Large Medical Model (LMM) vs Large Language Model (LLM)

The field of Artificial Intelligence has made significant advancements in recent years, and the most talked-about models are Large Language Model (LLM), but there are new healthcare specific approaches now. Underlying those models are neural network transformers. Transformers are designed to handle sequential data, such as language or time-series data, and are capable of learning complex patterns and relationships within the data. LLMs are transformers applied to sequential text data. However there is a lot more sequential data in healthcare than human readable text. That’s where GenHealth.ai’s Large Medical Models come in. While they share some similarities, they differ in several ways. In this blog post, we will explore the differences between LMM and LLM.

Large Medical Model (LMM)

Our first of a kind Large Medical Model or LMM for short is a type of machine learning model that is specifically designed for healthcare and medical purposes. It is trained on a large dataset of medical records, claims, and other healthcare information including ICD, CPT, RxNorm, Claim Approvals/Denials, price and cost information, etc. This information is often missing from the actual doctor notes or from research papers. Instead they are contained in standardized healthcare feeds like FHIR, HL7v2, and 837s. LMM is highly specialized, and its training is focused on medical data, which makes it more accurate and reliable than other models for certain applications and broadens the set of use cases to those which are beyond the human communicated text of healthcare.

Large Language Model (LLM)

LLM, on the other hand, is a type of machine learning model that is designed to understand and generate human language. It is trained on a large dataset of text, which could be anything from books to social media posts and research papers. LLM is capable of generating coherent paragraphs of text that are nearly indistinguishable from those written by humans. It has several applications, including language translation, chatbots, and automated content creation.

Differences between LMM and LLM

The primary difference between LMM and LLM is their training data. LMM is trained on medical data, while LLM is trained on language data. As a result, LMM is more accurate in diagnosing medical conditions and recommending treatment options, while LLM is more proficient in understanding and generating human language.

Another difference is their applications. LMM is mainly used in the medical field, while LLM has several applications, including language translation, chatbots, and automated content creation.

Emerging use cases

The main goal of LMM is to help the healthcare industry automate decisions based on individual patient histories, rather than rely on rules based solutions that prevail today. There are many use cases and markets that can benefit from using a large medical model to automate the billions of transactions that run healthcare behind the scenes. We are already seeing numerous companies in the industry take advantage of all the codified data to predict and manage patient futures via our LMM.

LMM and LLM are two important machine learning models that have different applications and training data. LMM is focused on medical data and is used to help health plans, pharma, providers, and others in the industry make more accurate diagnoses and treatment decisions. LLM, on the other hand, is focused on language data and is used for language translation, chatbots, and automated content creation that can also be fine tuned on healthcare conversations. Both models have their specific uses, and their development has the potential to change the way we live and work.