LLMs For Healthcare: Exploring the Current Scenario

Introduction

In recent years, large language models (LLMs have attracted significant attention in the healthcare sector. As interest in this technology expands, health-tech companies are exploring innovative ways to integrate generative artificial intelligence (GenAI) into clinical applications. Medical LLMs are enhancing clinical workflows, streamlining patient interactions, and improving diagnostic accuracy. This article will delve into the various LLMs being utilized in healthcare.

Overview

• Understand the capabilities and roles of LLMs like MedLM and BioMedLM in transforming AI healthcare solutions.
• Compare and see the differences between different LLMs for healthcare such as MedLM, MedAlpaca, BioMedLM, and more.
• Learn about the various applications of LLMs in Healthcare and their challenges.

Adoption of LLMs in Healthcare

Thanks to advancements in artificial intelligence (AI), the healthcare industry is undergoing a shift unlike before. Medical LLMs are one of the most useful among these advancements. They have completely changed how medical practitioners approach patient care, medical diagnosis, and biomedical research. Before diving into the topic’s meat, let’s quickly recap what LLMs are and what they do.

LLMs are AI-trained using billions of parameters and tons of text data. The famous transformer architecture is the backbone of this technology. In healthcare, LLMs support clinical decision-making by evaluating large amounts of medical data, enabling individualized treatment plans, and enhancing diagnosis accuracy.

Here are some other applications of LLMs in healthcare:

• They simplify administrative work at healthcare facilities by automating medical record summaries.
• They enable virtual healthcare via AI-driven chatbots and telemedicine platforms.
• LLMs expedite the discovery of new drugs and improve the training of medical personnel, increasing the effectiveness and accessibility of healthcare.

Also Read: Using Generative AI For Healthcare Solutions

Examples  of LLMs in Healthcare

The healthcare sector has always faced a myriad of issues, including the demand for individualized care, accessibility problems, and diagnostic errors. Several of these issues now have an answer in the form of LLMs.

Here are some of the most prominent LLMs specifically developed for Healthcare and currently used in the medical field.

Model	Developer	Year of Release	Parameters	Multimodal	Primary Use Case	Availability
MedLM	Google	2023	340B	✅	Medical question answering	Closed-source
RadOnc GPT	Meta	2023	70B	❌	Radiology image analysis	Open-source
MedAlpaca	Technical University of Munich	2023	13B	❌	Clinical data analysis	Open-source
GatorTron	NVIDIA	2021	3.9B	❌	Medical NLP	Closed-source
BioMedLM	Stanford University	2022	2.7B	❌	Biomedical research	Open-Source

Also Read: Application of Machine Learning in Medical Domain!

We will now explore two of the most advanced and used AI models in healthcare: MedLM and BioMedLM.

1. MedLM

MedLM has been specially fine-tuned for the healthcare industry. Google developed it, building on their previous developments with MedPalm and MedPalm2. It enhances medical documentation, clinical workflow, and clinical research. It helps healthcare providers manage operational and administrative tasks flawlessly, increasing the efficiency of healthcare staff. MedLM is available on Vertex AI.

Deloitte and Google Cloud work together to improve the member experience and reduce friction in finding care through an interactive chatbot. The chatbot helps health plan members better understand the provider options covered by their insurance plans.

2. BioMedLM

Stanford CRFM, in collaboration with MosaicML, developed BioMedLM, a domain-specific language model designed specifically for biomedical tasks. Trained in biomedical research papers and abstracts, the model excels in tasks such as question answering and summarization within the biomedical domain. With 2.7 billion parameters, it uses advanced techniques like Flash Attention to speed up training, completing a run on 300 billion tokens in just over six days.

BioMedLM performs state-of-the-art tasks like MedQA, which focuses on answering medical questions. Its custom tokenizer ensures that complex biomedical terms are processed efficiently. While the model has strong question-answering capabilities, its generative abilities are still being explored for future improvements. The release of BioMedLM is intended to foster further research and development in domain-specific applications, especially in areas like medicine, law, and finance.

Also Read: Machine Learning & AI for Healthcare in 2024

Applications of LLMs in Healthcare

The use of LLMs in healthcare has been on a constant rise, with new applications being discovered almost daily. This section will explore the various applications of LLMs in healthcare. We’ll examine how LLMs contribute to the field and discuss the resulting advancements in healthcare:

1. Empowering Healthcare Automation

One of the biggest challenges facing healthcare workers is the administrative load on them.

With LLMs, administrative responsibilities that burden healthcare personnel can be eliminated. Doctors can more effectively focus on patient care by automating tasks like Billing, appointment scheduling, and report generation.

1. Billing and Coding for Medicine: By automatically producing proper medical codes and billing documents based on an analysis of medical records. These LLMs can minimize errors and streamline the billing process.
2. Making Appointments: LLMs can power intelligent chatbots that can arrange appointments based on patient availability and provider schedules, increasing productivity and cutting down on wait times.
3. Report Generation: LLMs can examine patient data and produce draft reports that provide an overview of patient health status.

2. Specialized LLMs for Better Care

Specialized AI systems like AMIE (Articulate Medical Intelligence) are transforming healthcare by offering more accurate and reliable information than general-purpose LLMs like GPT-4. Trained in medical data, AMIE excels in diagnostic conversation, patient interaction, and multi-agent training.

1. Diagnostic: AMIE surpasses human physicians in diagnostic accuracy through its training in medical data.
2. Patient Interaction: With empathetic communication, AMIE enhances patient relationships while delivering critical diagnostic insights.
3. Multi-Agent Training: Training in multi-agent environments equips AMIE to simulate various patient interactions, improving precision and empathy in care delivery.

3. The Impact on Medical Research

LLMs transform patient care and drive biomedical research innovation. They accelerate the discovery of new biological models, leading to novel therapies. LLMs can also help predict drug compound properties, aiding early-stage screening of candidates.

1. Biomedical Research: LLMs are aiding the discovery of new biological models, speeding up experimental validation and therapeutic development.
2. Drug Research: LLMs can help create hypotheses for research that Healthcare professionals can use to initiate their research.

4. Advancements in Telemedicine

Providing healthcare remotely via telecommunications technology, or telemedicine, has become increasingly popular, especially post-pandemic. LLMs can power virtual assistants that can easily organize appointments, respond to inquiries around the clock, and even translate between languages in real-time during consultations.

Here are three ways in which LLMs can help with Telemedicine:

1. Virtual Assistant: LLMs power virtual assistants who can respond to patients’ inquiries about symptoms, prescriptions, and appointments. In addition to scheduling appointments, these AI assistants can offer basic triage assistance.
2. Real-time Language Interpretation: LLMs can bridge language barriers and communicate clearly between patients and healthcare providers from diverse origins during teleconsultations.
3. Emotional Dissection: During a teleconsultation, LLMs can identify a patient’s tone and emotional distress, which helps healthcare providers understand the patient’s sentiment and offer better support.

Challenges in Implementing LLMs in Healthcare

While LLMs positively impact healthcare, they present multiple challenges regarding implementation. Here are some of the most common challenges medical practitioners and institutions face while implementing LLMs:

1. Complexities in Fine-Tuning

Large-scale neural networks, or LLMs, are simply enormous graphs of related facts and weights. Although a large number of general-purpose LLMs possess some healthcare knowledge, they also draw on outside data, such as consumer behaviour and prejudices. This makes it difficult to fine-tune these models for medical applications.

Careful tuning and tweaking data is one of the best ways to prevent this. One of the best ways to solve the complexities of fine-tuning is by carefully tweaking the dataset. This would allow medical practitioners to weed out inaccurate or useless data and guarantee that the models generate relevant and accurate results.

2. Unexpected Outcomes as a Result of Information Drift

LLMs continuously receive new data, which can cause information drift or model drift. LLMs in the healthcare industry, particularly those offered by major commercial corporations, are susceptible to continuous changes in their knowledge base, unlike traditional AI healthcare solutions and machine learning systems, where data control is more managed. Because of this drift, keeping the model accurate and reliable over time may be challenging because its underlying data may change.

3. Following Ethical Considerations

LLMs are increasingly becoming a part of every industry, and their role in healthcare holds great potential. The primary aim of using LLMs in healthcare is to minimize human error and ensure that tasks are performed ethically, safeguarding all forms of data. Practising responsible AI when implementing LLMs in healthcare is crucial to avoid harm to data or its sources.

Failing to follow responsible AI practices can result in data loss, increased access disparities, a negative user experience, and damage to LLMs’ credibility and reliability. To prevent these issues, it is essential to remain open to feedback and conduct thorough rounds of testing before deploying LLMs in healthcare settings.

Conclusion

Unquestionably, large language models are transforming the healthcare industry. Their influence is far-reaching, from speeding up biomedical research to improving diagnostic accuracy. One thing is certain as we continue to investigate the potential that these models present: healthcare will evolve in tandem with LLMs. As these technologies develop further, they can change our perception of medicine as a whole and enhance patient treatment.

Must Read: Evolution of AI in Medicine with Dr. Rodolphe Katra

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