2023 was the year of generative AI and the large language models will continue to leave an impact in 2024.
The ability of a large language model (LLM) to understand large data sets and extract useful information from them can disrupt the healthcare industry. Large language models are trained to anticipate the next word in a sentence, summarize texts, or answer questions. These capabilities allow the model to perform tasks such as documentation, simple radiology reports, or extracting drug names from the database.
The use of a large language model in healthcare is undergoing research, but here’s what we’ve extracted for you.
What Research Has to Say About LLM in Healthcare?
The National Library of Medicine has published a research paper that highlights examples of LLM fine-tuned for healthcare applications.
1. BioBERT
BioBERT is a pre-trained biomedical language model that is based on the BERT architecture. It is refined using biomedical corpora, consisting of PMC articles and PubMed abstracts.
BioBert is used for NLP (Natural Language Programming) tasks such as
- Answering queries
- Relation extraction
- Named entity recognition
2. ClinicalBERT
ClinicalBERT is a domain-oriented model and it is fine-tuned on the MIMIC-III dataset that includes EHRs from intensive care unit patients.
This model is used in clinical NLP tasks such as:
- Diagnosis classification
- Patient mortality prediction
- Data anonymization
3. BlueBERT
BlueBERT is also based on the BERT architecture and is pre-trained on biomedical text data.
Types of tasks performed by BlueBERT are:
- Answering biomedical queries
- Relation extraction
- Named entity recognition
Expert Opinion on Large Language Model in Healthcare
Source: Nature
Here’s what JAMA Network, Forbes, and Nature are talking about LLM applications in healthcare.
1. Clinical Text Generation
Large language models are good at predicting the next word or sentence. With the surplus data fed into the system and pre-trained for it, it is a cakewalk for this technology.
Suppose you want to ask about a treatment plan, LLM can anticipate what you want to say before completing the sentence and provide clinical notes on the topic. This saves ample time for the physicians to look for specifications. They can even summarize the whole text.
2. Summarizing and Translating Medical Notes
Language barriers may hinder patient participation in their healthcare and well-being. But, with a large language model, physicians can translate text or spoken language into their own quickly, ensuring the participation of both providers and patients in clinical decision-making.
Moving on, the healthcare sector produces tons of patient data daily, and analyzing each of them can be a cumbersome task. However, LLMs can extract vital information about any patient, drug, disease, or specific treatment plan and summarize it for the physicians to read. Moreover, LLMs can summarize patient questions in one line for the doctors.
3. Medical Question Answering
Just like ChatGPT, large language models can also answer health-related queries. From symptom checking to type of diagnosis, including predictive health outcomes, any form of question is answered by the model.
Providers or patients can expect answers promptly without delay, however, it is essential to check the authenticity and credibility behind those answers.
4. Medical Research
Healthcare providers dive into the latest medical research to offer the best quality care to their patients. Large language models can help to summarize scientific notes, concepts, and evidence, enabling physicians (and even medical researchers) to access resources quickly. However, the quality of these notes highly depends on the training data offered to the LLM.
Large language models may not provide up-to-date content, their knowledge may be static and can prevent them from being the primary source of information for research. However, with real-time updates, the value of LLMs as sources can be boosted significantly.
Additionally, LLMs in healthcare can help to discover new medical research trajectories by unlocking many connections between scientific literature. The summary feature can make it easier to sort out information and extract only the necessary ones.
5. Documentation
Documentation consumes about 25 percent of providers’ workday. Large language models can assist in the creation of standardized and concise reports. They can convert unstructured notes into a structured format, thus simplifying the documentation process.
Furthermore, another ability of large language model is to produce both written and spoken language, thus promoting automated dictation and prompts. All of these benefits of LLM in healthcare can alleviate physicians’ documentation burden, decrease cognitive load, and increase their efficiency in treating patients.
Wrapping Up
LLM are expected to have a significant effect on healthcare. Nevertheless, it’s important to be aware of their limitations.
LLM in healthcare have demonstrated a tendency to replicate existing biases, and are prone to hallucinating and disseminating false information.
There are currently no mechanisms in place to guarantee that the output of a large language model is accurate. This significantly restricts its applicability to clinical settings, where mistakes and misinformation could be life-altering.
The lack of accountability on the part of LLMs exacerbates this problem. Safety guardrails built into LLMs may also be a limitation in their own right. For example, bias prevention could lead to different symptoms being overlooked among men and women.
In general, recent updates to versions and models specifically designed for medical applications, with training on medical data, show promising progress. However, before application of large language model in healthcare, central conditions like safety, validity, and ethical concerns need to be addressed. Incorporation of compliant measures is the way forward with large language models in healthcare.
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