Medical Large Language Models: Bridging Technology and Healthcare

Konstantin Kalinin

Head of Content

March 18, 2024

So, you’re thinking of simply plugging in a large language model, connecting it to the web, and voila, expecting it to churn out accurate diagnoses, treatment plans, and more? Well, that might be a tad ambitious, even for the most optimistic among us.

Yet, it’s undeniable that this technology holds breakthrough potential for the medical field. The cautious yet promising applications of large language models in healthcare have shown us a glimpse into an exciting future. In this blog, we’ll delve into different LL models and explore their potential usefulness in medicine.

Top Takeaways:

• Breakthrough Potential: A medical large language model is not just another tech fad. These models have the potential to revolutionize the medical field, offering new ways of diagnosing diseases, developing treatments, and improving patient care.
• Information Retrieval Methods are Key: Large language models for healthcare shine brightest when applied using information retrieval methods. Verifying answers with rigorously vetted healthcare databases and reputable online sources can add an extra layer of reliability and accuracy to these models. Think of it as a safety net, ensuring your AI doesn’t go off the rails.
• Exciting Possibilities Ahead: With a medical LLM model, the possibilities are virtually endless. From automating routine tasks to providing personalized care recommendations, these models can transform how we approach healthcare.

Table of Contents:

1. Evolution from Basic Models to Advanced LLMs like GPT-4
2. Understanding the Role of LLMs in Healthcare
3. Diverse Applications of LLMs in Healthcare
4. Healthcare Large Language Model Case Studies
5. Ethical and Legal Considerations for LLMs in Healthcare
6. Challenges with Implementing Medical Large Language Models
7. Ready to Innovate with LLM Models for Healthcare?

Evolution from Basic Models to Advanced LLMs like GPT-4

The journey from the early, rudimentary chatbots to the sophisticated nuance of GPT-4 is truly revolutionary! How did we leap from basic pattern recognition to AI that grasps the complexities of human language and medical terminology with such precision? Let’s explore the transformative evolution of large language models and uncover the breakthroughs reshaping patient care and medical research. Let’s dive in!

The Evolution of AI in Healthcare: Focusing on NLP

Artificial Intelligence made its way into healthcare, starting with basic tasks like data entry and pattern recognition. But as technology progressed, so did AI’s role in healthcare. A key milestone in this evolution was the emergence and use of Natural Language Processing (NLP), a specialized branch of AI that aids computers in understanding, interpreting, and generating human language.

Here are some pivotal moments in the timeline of NLP’s integration into healthcare:

• Late 1960s: The earliest applications of NLP in healthcare were rudimentary processing systems designed to translate medical terms into codes for databases.
• 1980s: Expert systems using NLP started emerging, assisting doctors with decision-making by providing suggestions based on coded rules.
• Early 2000s: Statistical methods for NLP became popular, enabling more accurate interpretation of electronic health records.
• 2013: The introduction of Word2vec, a neural network-based model for NLP, marked a significant breakthrough, allowing machines to understand context and meaning in text data.
• Late 2010s: Transformer-based models, such as GPT and BERT (Bidirectional Encoder Representations from Transformers), revolutionized NLP by understanding the context of words in a sentence in all directions.

When exploring LLMs in healthcare, a key focus is the notable shift from pre-trained language models (PLMs) like BERT and RoBERTa to increasingly sophisticated large language models like ChatGPT.

Despite being a significant advancement in AI, PLMs did not show much potential for enhancing real-world clinical workflows. They were plagued by the following limitations:

• primarily served as single-task systems
• exhibited limited expressiveness and interactivity (less effective in intricate AI tasks such as regression or classification)
• required too much data for training and lacked robustness
• focused on fine tuning the prompts and model pre-training

The advent of LLMs has revolutionized AI in healthcare, overcoming PLMs’ limitations and ushering in a shift towards generative AI approaches and data-centered methodologies.

Rather than concentrating on tweaking the neural architecture of PLMs as you build a natural language processing application, newer LLMs excel in grasping existing data, emphasizing the crucial role of high-quality data in enhancing healthcare applications.

The Advent of Large Language Models

The emergence of Large Language Models (LLMs) like GPT-3 and GPT-4 has redefined the boundaries of what AI can achieve in healthcare. These models, with their deep learning capabilities and advanced understanding of natural language, have made it possible to process vast amounts of unstructured text data, providing unprecedented insights and decision-making support in health care.

Today’s progress in LLMs is driving a multimodal trend, supporting EHRs, medical images, and health data streams. This shift has unlocked fresh possibilities for research and applications in healthcare. As we push forward with LLMs, tackling the task of systematically gathering matched multimodal data is of utmost significance.

Here are some of the key LLMs making waves in healthcare:

• BioBERT: A BERT-based model pre-trained on large-scale biomedical corpora, BioBERT has shown promise in several biomedical NLP tasks, including disease prediction and drug-drug interaction extraction.
• ClinicalBERT: Tailored for clinical text, ClinicalBERT has been used for tasks such as predicting hospital readmission and mortality rates.
• GPT-3: OpenAI’s GPT-3 has 175 billion machine learning parameters and has demonstrated potential in medical diagnosis and treatment recommendations.
• GPT-4: The latest in the GPT series, GPT-4’s capabilities are still being explored, but it is expected to bring even more accuracy and sophistication to tasks such as medical text analysis and summarization.
• GatorTron: A new addition to the medical LLM family, GatorTron is advancing clinical language understanding. Developed at the University of Florida, these LLMs have been trained on a vast dataset of over one billion clinical notes.
• Med-PaLM 2: Anticipated advancements in the medical field. Building on its predecessor, Med-PaLM 2 aims to enhance medical language understanding for the healthcare industry.
• HuatuoGPT: This cutting-edge model is named after an ancient Chinese physician known for his surgical skills. True to its namesake, HuatuoGPT operates with surgical precision in understanding and generating medical text.
• XLNet: With its unique permutation-based training method, XLNet surpasses traditional LLMs by capturing bidirectional context. This means it can predict the sequence of medical data more effectively.
• ClinicalGPT: Tailored specifically for the clinical domain, ClinicalGPT bridges the gap between generalized GPT models and the specialized language of healthcare professionals.

While the models mentioned indeed represent some of the best medical large language models, they are not the only solutions. There are plenty of LLMs optimized for clinical settings, both proprietary and publicly available.

In addition, depending on the unique needs of a project, developing a custom model from scratch may be necessary to achieve specialized objectives sometimes not covered by off-the-shelf options.

As we continue to innovate and push the boundaries of what’s possible with LLMs in healthcare, the future looks incredibly promising.

Understanding the Role of LLMs in Healthcare

As we continue our exploration of Large Language Models (LLMs) in healthcare, it’s essential to understand their role and the transformative impact they’ve had on the industry. With the advent of healthcare LLM models, we’ve moved beyond the realm of simple data analysis and stepped into an era where AI can generate human-like text based on the information it’s been trained with.

But how does this work? The secret lies in the training data used to educate these LLMs. By feeding them massive amounts of text data – such as electronic health records, medical literature, or patient interactions – these models learn to understand, interpret, and generate text much like a human would. This process, known as machine learning, forms the basis of their operation.

What’s more, thanks to natural language processing, LLMs can understand the context of the language, making them particularly useful in the complex world of healthcare. Let’s explore three key areas where LLMs are making a significant difference:

Clinical Decision and Administrative Support

Imagine a world where healthcare professionals have an AI assistant by their side, providing real-time insights into patient symptoms, suggesting possible diagnoses, and even recommending treatment plans. That’s exactly what LLMs are enabling today. They’re being used to aid healthcare professionals in decision-making and symptom analysis.

But that’s not all. LLMs are also transforming administrative tasks. They’re helping automate mundane tasks like scheduling appointments, transcribing notes during a patient visit, and even handling lab or refill requests. This not only streamlines the process but also allows healthcare providers to focus more on patient care.

Information Management

The healthcare industry, including public health, is inundated with vast amounts of data, ranging from scientific literature and clinical trial data to various healthcare data sources. Digesting this information manually would be a herculean task. Enter LLMs. They’re being used to aggregate and synthesize these data, helping healthcare professionals find meaningful insights quickly and efficiently.

In addition, LLMs are also redefining medical education. They can explain complex medical concepts in simple terms, making learning more accessible for everyone.

In an impressive feat that underlines their sophistication, many LLMs are showcasing their efficacy by successfully passing stringent tests like the US Medical Licensing Examination (USMLE), often considered a cornerstone in assessing medical knowledge and decision-making abilities.

Communication

Personalized patient communication is another area where LLMs are showing promise. These models can tailor the conversation based on the specific audience. For instance, a chatbot powered by an LLM could provide different information to a diabetes patient than it would to a heart disease patient, depending on their background knowledge and the context of the conversation.

In conclusion, Large Language Models are playing a crucial role in modern healthcare, from decision-making support and administrative tasks to information management and personalized communication. As we delve deeper into their diverse applications in the following section, you’ll see just how transformative these models can be.

Want to create a ground-breaking application that could revolutionize patient care? Learn how to create a healthcare app with our comprehensive guide.

Diverse Applications of LLMs in Healthcare

LLMs are transforming healthcare in numerous ways, from analyzing medical text to predicting patient readmissions. Let’s explore the diverse applications of LLMs in healthcare, particularly focusing on the LLM for the medical domain.

Automating Mundane, Repetitive Tasks

LLM-based chatbots, for example, applications of ChatGPT in healthcare, can automate administrative tasks such as scheduling, medical data entry, and summarizing medical notes into intake fields, freeing healthcare professionals to focus on patient care.

Niche use cases:

• a concierge chatbot responsible for patient intake
• AI platform that frees providers from admin tasks altogether
• a bot that records and transcribes provider-patient consultations, turning all patient data into a structured (AI-processable) EHR record

Read more on patient intake automation

Data Harvesting in EHRs

Did you know that about 80% of data in EHRs is unstructured narrative? That includes crucial determinants like social and environmental factors, drug adherence information, and much more about patients. This data is a goldmine for healthcare applications, especially for tasks such as clinical trials. However, extracting this data manually can be both time-consuming and error-prone. That’s where LLMs come in.

LLMs can extract and analyze information from unstructured text in EHRs, interpret complex medical language, and even identify trends or patterns in the data.

Niche use cases:

• exploring health trends across diverse demographics
• selecting candidates for clinical trials

Medical-Specific Questions and Drafting Patient-Doctor Conversations

Healthcare software powered by an LLM, such as MedLM, specifically designed for medical purposes, can be utilized for answering medical-specific questions or drafting patient-doctor conversations into meaningful medical SOAP notes.

Niche use cases:

• a bot providing patients with personalized medical information and medical advice based on their symptoms and medical history
• an AI scribe assisting doctors in drafting comprehensive SOAP notes quickly and efficiently

Assisting in Medical Research by Analyzing Datasets

Using LLMs, researchers can analyze large medical datasets to identify patterns, generate hypotheses, and accelerate the pace of medical discoveries.

Niche use cases:

• utilizing LLMs to analyze data and identify potential treatments for rare diseases
• using LLM-generated insights to guide research and development of new drugs or therapies

Ongoing Patient Engagement through Natural Conversation

GPT models, coupled with trained models specific to the patient population, can facilitate ongoing patient engagement that relies on natural conversation, thereby incentivizing ongoing data collection.

LLM-powered medical chatbots provide real-time responses to patient queries, facilitate health education, and support patient engagement in their healthcare journey.

Niche use cases:

• An RPM (remote patient monitoring) chatbot assisting patients in following their treatment plan, medication intake, and collecting their health metrics.
• A mental health chatbot providing support and resources for patients dealing with depression, anxiety, or other mental health issues.

If you’re interested in leveraging AI technology for personalized patient communication, explore our guide on medical chatbot development.

Clinical Decision Support Systems

Clinical LLMs like Glass Health and open-source platforms like Med42 utilize clinical data to determine possible diagnoses and treatment plans, providing invaluable support in clinical decision-making.

Niche use cases:

• An ER triage chatbot identifying high-risk patients and prioritizing their care based on critical factors.
• A LLM-based platform that optimizes medication management for patients with chronic conditions, reducing the risk of adverse drug reactions and improving patient outcomes.

Read more on clinical decision support systems implementation.

Drug Discovery and Clinical Trials

LLMs can expedite the drug discovery process by analyzing scientific literature, predicting drug interactions, and optimizing clinical trial design.

Niche use cases:

• Using LLMs to identify potential new drug targets and predict the efficacy of existing drugs for different diseases.
• Generating insights from large volumes of clinical trial data to optimize study design, reduce costs, and improve success rates.

Predicting Patient Readmissions Using EHR Records

By analyzing EHR data, LLMs can predict patient readmission risks, enabling proactive interventions.

In conclusion, LLMs are a game-changer in healthcare, offering diverse applications that streamline operations, improve patient care, and drive medical advancements.

As we delve deeper into real-world implementations of Healthcare LLMs in our next section, you’ll get a clearer picture of their transformative power in action.

Related: AI app development

Healthcare Large Language Model Case Studies

The healthcare industry is witnessing a paradigm shift with the advent of large language models. These advanced AI models in medicine are transforming patient care, clinical decision-making, and operational efficiency in remarkable ways. Let’s delve into some real-life applications of healthcare large language models.

Using LLMs for Trend Identification in Medical Records

Epic, a leading EHR provider, partnered with Microsoft to integrate GPT-4 into their EHR system. This collaboration aims to identify trends in medical data, thereby enabling more personalized patient care. The integration of Microsoft’s Azure OpenAI Service with Epic’s EHR platform extends natural language queries, opening up new possibilities for data analysis in healthcare.

Related: How to Integrate your health app with EHR/EMR 

Glass Health’s Clinical LLM: Diagnosis and Treatment Planning

Glass Health’s clinical LLM demonstrates how AI can revolutionize patient diagnosis and treatment planning. By using clinical data such as patient summaries, the system can suggest possible diagnoses and treatment plans, showcasing the potential of LLMs in clinical decision support.

Expanding upon its impressive foray into AI-assisted healthcare, Glass Health recently made headlines with an astonishing seed-funding round ($5M), bolstering its valuation and underlining the market’s confidence in its visionary approach.

In a recent demonstration that piqued interest among healthcare professionals, Dr. Aaron Neinstein, VP of Digital Health at UCSF, showcased Glass Health’s capabilities to the Department of Medicine. The buzz is real; Dr. Neinstein emerged from the demonstration with a sense of promise, intrigued by the platform’s potential to improve diagnosis and treatment planning.

We also recommend you take a closer look at these open-source LLM platforms for CDS:

• Med42
• Hippocratic AI

Google’s AIME: Empathetic Conversations and Diagnostic Questioning

Google’s AI model for empathetic interactions (AIME) is designed to facilitate empathetic conversations and diagnostic questioning. Google claims that it’s more empathetic and accurate than real-life doctors, indicating how LLMs can enhance patient-doctor interactions.

Google’s researchers have broken new ground, detailed in their compelling study, “Towards Conversational Diagnostic AI,” which exposes AMIE’s prowess in medical interviews. Demonstrating remarkable accuracy, AMIE eclipsed human benchmarks across 28 of 32 assessment criteria – a herald of AI’s burgeoning role in healthcare diagnosis.

Yet, with innovation comes caution, and the Google team emphasizes the need for further research to translate AMIE’s potential into clinical reality. Could we be on the cusp of redefining patient-doctor dialogue?

NYU Langone Health: Harnessing LLMs for Predicting Patient Readmissions

NYU Langone Health, in collaboration with NVIDIA, has pioneered the use of LLMs to predict patient readmissions within 30 days of their initial hospital discharge. This innovative approach, underpinned by a model named NYUTron, offers a significant step forward in predictive healthcare.

Hospital readmission is a significant concern in healthcare systems globally. In the U.S., nearly 15% of hospital patients find themselves readmitted within 30 days of discharge. Such readmissions often correlate with poorer patient outcomes and increased costs for both patients and healthcare providers.

Deployed across six of the healthcare system’s inpatient facilities, NYUTron provides physicians with AI-driven insights, enabling them to identify patients who may benefit from clinical interventions designed to reduce readmission likelihood.

ChatGPT-4: Improving Clinician Diagnostic Performance

Researchers from Beth Israel Deaconess Medical Center made a groundbreaking discovery — ChatGPT-4 outperformed clinicians in estimating diagnosis probabilities both before and after laboratory testing.

What makes this even more fascinating? When provided with lab test results, ChatGPT-4’s performance improved significantly. Lab tests play a crucial role in diagnosis, providing objective data about a patient’s health status. With the integration of these results, ChatGPT-4 could refine its predictions, further enhancing its diagnostic prowess. However, it’s essential to remember that while ChatGPT-4 has shown promising results, it doesn’t replace human clinicians but augments their skills.

Drug Discovery and Clinical Trials

LLMs are being used to expedite the drug discovery process and optimize clinical trial design. By analyzing scientific literature, predicting drug interactions, and assisting in trial design, LLMs are paving the way for faster, more effective drug development.

Here’s an interesting fact: In early 2020, Exscientia, a leading AI-driven pharmatech company, announced the first-ever entry of an AI-designed drug molecule into human clinical trials. This was a significant milestone in the integration of AI into healthcare, demonstrating the potential of LLMs to enhance drug discovery.

The use of LLMs in healthcare is rapidly evolving, with new applications continually emerging. As LLM technology advances, so too does the potential for improved patient outcomes, operational efficiencies, and overall healthcare delivery.

Curious about the investment needed for these technological advances? Find out about the cost of AI in healthcare in our detailed analysis.

Read more on clinical trial software development and medical device clinical trials.

Ethical and Legal Considerations for LLMs in Healthcare

The advent of large language models in transforming healthcare has undoubtedly brought about numerous benefits, from improved diagnostics to personalized patient care. However, alongside these advancements, it’s imperative to consider the ethical and legal implications as well as health inequities that come with the integration of such technologies.

Complying with HIPAA and Protecting Patient Data

One of the most critical considerations is the protection of patient data. Any application of LLMs in healthcare must comply with health data protection regulations, such as the Health Insurance Portability and Accountability Act (HIPAA) in the U.S. After all, you can’t just build a chatbot like ChatGPT for a medical use case and expect it to be immediately HIPAA-compliant.

Any accidental or intentional disclosure of health information into LLM technology could constitute a notifiable data breach. So, it’s not just about following the rules—it’s about ensuring robust data anonymization and security protocols are in place to maintain patient privacy and confidentiality.

But how do you make sure a health app is secure and HIPAA compliant while using LLMs? Let’s break it down:

• Encryption: Encrypt all sensitive data, both at rest and in transit. This ensures patient safety even if a breach occurs, the data remains unreadable to unauthorized individuals.
• Access Controls: Implement strict access controls based on roles and responsibilities. Limit who can access the data and what they can do with it. In addition, developers should not have open access to PHI.
• Regular Audits: Conduct regular audits to identify any potential vulnerabilities and assess the effectiveness of current security measures.
• Training: Ensure all personnel involved in handling the data are adequately trained on HIPAA regulations and the importance of data security.
• Data Anonymization: Whenever possible, use anonymized data. If the data doesn’t contain identifiable information, it can’t be traced back to an individual.
• Incident Response Plan: Have a plan in place for identifying, managing, and reporting any data breaches. Quick and effective responses can mitigate the damage caused by a breach.

As we continue to find innovative ways of using LLMs in healthcare, the question of how to deploy these models without storing PHI becomes increasingly important.

Here are some practical strategies for deploying machine learning models without storing PHI:

• Federated Learning: This is an approach where the model is trained across multiple decentralized devices or servers holding local data samples without exchanging them. This means that the PHI never leaves its original location, providing an additional layer of security.
• Data De-identification: Before using any patient data to train your LLM, consider anonymizing it. This process involves removing any identifiable information, making it impossible to trace the data back to an individual.
• Differential Privacy: This technique adds a small amount of random noise to the data, which provides a level of privacy while still allowing for accurate analyses.
• On-device Processing: Instead of sending data to a central server, process data directly on the user’s device. This not only minimizes latency but also ensures that sensitive data doesn’t need to be transmitted or stored elsewhere.
• Use Synthetic Data: Synthetic data is artificially generated data that mimics the characteristics of real-world data. It can be used to train machine learning models without exposing any real PHI.

Related: HIPAA Compliant App Development Guide

LLMs can assist in handling tasks like de-identifying PHI. For instance, a specific LLM named DeID-GPT can automatically detect and eliminate identifying patient info.

WHO’s AI Ethics Guide for Generative Models

The World Health Organization has recognized the ethical implications of AI technologies, including LLMs, and released guidance on AI ethics and governance for large multi-modal models. This guide emphasizes the need for transparency, accountability, inclusivity, and fairness in the design and use of AI models. It also warns about the potential misuse of LLMs to generate and disseminate disinformation.

Currently, no specific laws regulate the use of AI in healthcare. However, given the rapid advancements in this field, it’s likely that we’ll see more defined legal frameworks in the future. As such, it’s crucial for healthcare organizations to stay abreast of emerging ethical guidelines and legal developments in this space.

Navigating the ethical and legal landscape of LLMs in healthcare can be complex. It’s crucial to balance the potential benefits of these technologies with the need to protect patient data, ensure equity, and adhere to ethical guidelines. By doing so, we can harness the power of LLMs to transform healthcare while safeguarding the rights and interests of patients.

Discover how we leverage cutting-edge Generative AI technology to empower healthcare providers and enhance patient care.

Challenges with Implementing Medical Large Language Models

As we venture further into the realm of artificial intelligence and machine learning, large language models (LLMs) are making their mark in healthcare. These powerful tools can revolutionize patient care and optimize operations. But, implementing LLMs, especially those trained on medical data, is not without its challenges.

Let’s unpack some of the hurdles you may encounter:

• Data Privacy: The most pressing concern when working with LLMs in healthcare is data privacy. How do we ensure that the vast amounts of data used to train these models comply with regulations like HIPAA? How can we guarantee that sensitive patient information is protected at all times?
• Model Transparency: Understanding how an LLM makes decisions can be a complex task. This lack of transparency can make it difficult for healthcare providers to trust the predictions or suggestions made by these models. How can we build trust in an AI system when its decision-making process is somewhat of a black box?
• Bias in AI: LLMs learn from the data they’re trained on. If this data is biased, the model will also be biased. This could lead to disparities in healthcare delivery. How can we ensure that our models are fair and unbiased?
• Integration with Existing Systems: Integrating an LLM into existing healthcare systems can be challenging, particularly given the complexity and diversity of these systems. How do we seamlessly integrate these models to enhance, rather than disrupt, workflows?
• Maintaining Accuracy Over Time: Healthcare is a rapidly evolving field. An LLM trained on medical data today might not be accurate tomorrow. How do we ensure that our models stay up-to-date and relevant?
• Resource Intensive: Training LLMs, especially on medical data, requires significant computational resources and expertise. How can startups and smaller organizations overcome these barriers?

Despite these challenges, the potential benefits of integrating LLMs in healthcare are immense. The key lies in partnering with the right team, one that has a deep understanding of both AI and healthcare, and can navigate these challenges to deliver a solution that is safe, effective, and compliant.

Ready to Innovate with LLM Models for Healthcare?

At Topflight, we’ve spent years at the forefront of AI in healthcare, developing solutions that not only meet regulatory standards but also deliver real value to patients and healthcare providers.

Let’s take a moment to ponder. What does it truly mean to innovate with large language models in healthcare today? Is it merely about integrating an AI model into your system and hoping for the best? Or is there a more thoughtful, strategic approach to this?

In our experience of partnering with healthcare providers who are on the cutting edge of applying LLMs in healthcare, we have found that the most successful implementations:

• Leverage retrieval mechanisms for data verification: This could involve using proprietary curated healthcare databases or carefully vetted web sources/medical journals to ensure the information being processed by the LLM is accurate and reliable.
• Foster multimodal use case scenarios: For instance, an LLM could be used to write reports for radiologists based on image analysis.
• Automate mundane, routine tasks: By taking over time-consuming tasks, LLMs can free up physicians to focus on what they do best—providing primary care to their patients.
• Proactively ask questions from patients for clarifying info: An intelligent chat feature could be used to gather more detailed information from patients, enhancing the accuracy and usefulness of the LLM’s output.

In other words, creating a truly useful healthcare app that runs or includes an LLM isn’t just about the technology. It’s about how you use that technology to solve real-world problems and make life better for both healthcare providers and their patients.

At Topflight, we understand the intricacies of integrating LLM models for healthcare. Our team has the technical expertise and industry knowledge to help you navigate the complexities of integrating LLMs into your healthcare app. We’re ready to help you turn this challenge into an opportunity for innovation and growth. So, are you ready to take that leap? Let’s shape the future of healthcare together.