Large Language Models in Healthcare

There is a critical need for guidance on integrating large language models and generative AI in healthcare and medical practices using technologies such as reinforcement learning from human feedback (RLFH), few-shot learning, and chain-of-thought reasoning, according to a PubMed study.

For example, large language models can be useful in clinical note summarization and achieve higher accuracy. Having the right LLM potentially enables excellent performance in extracting essential information and generating clear, concise summaries from complex clinical documents, enabling efficient review and understanding by healthcare professionals.

Such capabilities profoundly raise the utility and advantages of Artificial Intelligence as opposed to traditional, rule-based systems. They also encourage inclusive collaboration with relevant stakeholders, from clinicians to consumers. LLMs enhance data management, information retrieval, and decision-making.

Innovation in data acquisition, fine-tuning LLM, prompt engineering, LLM evaluation, and system implementation are all crucial elements of realizing the benefits of these technologies. The PubMed study recommends proactively engaging with foundation models to provide high-quality healthcare service, patient safety, and efficiency while following ethical and legal guidelines for responsible governance.

Large Language Models play a crucial role in administrative tasks such as clinical practice and documentation, medical research, and patient care. Medical large language models elevate patient care by analyzing vast amounts of medical literature, clinical records, and scientific papers with precision and speed. They provide valuable insights, aid in accurate diagnoses, assist in treatment planning, and support healthcare professionals in making informed decisions.

Virtual Medical Assistants

68 million adults in the USA have two or more chronic diseases, demanding a tenfold increase in care capacity. LLM-powered AI assistants can enhance telemedicine by understanding and responding to patient queries, providing medication reminders, offering general health information, and helping with medical question answering to improve patient engagement.

Clinical Documentation and Electronic Health Records

Leveraging large language models in the healthcare industry can efficiently summarize extensive patient notes, reports, and medical histories, assisting healthcare professionals in quickly extracting relevant information and insights.
This not only saves substantial time spent on manual data analysis and documentation but also enhances accuracy. This enables the healthcare providers to redirect their focus and energy towards more personalized and impactful patient care.

Adverse-Event Detection

According to the National Library of Medicine Sources of Safety Data and Statistical Strategies for Design and Analysis: Postmarket Surveillance, “LLMs can potentially automate the detection of adverse events from electronic health record data and may support surveillance of drug safety in the post-marketing setting using new data sources.”

There have been impressive advances in understanding natural language processing following the release of context-aware deep learning models such as bidirectional encoder representations from transformer architecture and models like (BERT) and now GPT-4. However, the presence of identifiable patient information in clinical notes obstructed research in this area.

The future of large language models (LLMs) stands as a beacon of transformative potential for healthcare providers. These advanced AI systems, harnessing the power of vast data and sophisticated algorithms, promise to revolutionize how medical professionals diagnose, treat, and manage patient care, leading to a new era of personalized and efficient healthcare services.

Predicting cancer metastasis

LLMs are now being used to predict cancer metastasis and to assist in devising clinical treatment responses. Cancer-progression patterns are extracted from vast numbers of computed tomography reports for analysis by a natural language processing model; they can then predict metastatic disease in multiple organs. This model and its treatment data can point the way toward specific therapies and enable a better understanding of population-level cancer treatments and how drugs work in the real world.

Conversational AI Diagnostics

Large language models are being integrated into predictive systems, especially in the field of medical imaging to deliver images with high diagnostic accuracy. It can potentially go even further by integrating screening and diagnosis with the LLM-powered Conversational AI platforms.

This approach would allow a clinician to converse with and interrogate a system, draft a report, or improve an existing report. Integrating LLMs and conversational AI in healthcare including in clinical and medical settings, and systems that accurately read mammograms and other images are becoming more feasible every year.

Drug Discovery and Development

The use of LLMs especially generative AI in pharma and biotech industry can drive substantial competitive advantages, spanning cost efficiency, accelerated timelines, and enhanced innovations in clinical trials.

As LLMs evolve by incorporating empirical data and domain expertise, these models can be fine-tuned further to provide valuable insights and solutions. With their ability to analyze large volumes of data, LLMs have the potential to help predict drug interactions, side effects, and efficacy, expediting the drug discovery process and driving cost reductions.

As organizations explore the capabilities of an enterprise LLM and implement Artificial Intelligence solutions that advance us toward a future where healthcare is increasingly personalized and effective, it is important to consider the following factors:

• Intended use case
  Consider whether you will be using the LLM for content generation, classification, translation, etc. Which are the core tasks you expect it to perform?
• Data Domain
  If you are a healthcare organization, you will want to have a domain-specific LLM, that is pre-trained on relevant and highest-quality data.
• Ethical considerations
  Educate yourself on potential biases, safety considerations, and risks of misuse depending on your use case.
• Accuracy
  Will you need high precision or is moderate accuracy acceptable? Larger models are generally more accurate.
• Scalability
  Are you considering a large number of users? How many thousands of queries per second do you anticipate? Scaling can be costly.
• Cloud vs on-premise
  Cloud API access can bring you ease and convenience but on-premise offers greater control and opportunity for customization.
• Budget
  Consider the range of pricing based on compute usage, queries, and model size. Weigh cost and capabilities.
• Inference speed
  Smaller models may be faster. How important is real-time low latency?

According to Nigam Shah, PhD, MBBS, chief data scientist for Stanford Health Care, “We call it ‘LLM bingo,’ where people check off what these models can and can’t do. ‘Can it pass the medical exams? Check. Can it summarize a patient’s data and history?

Check.’ While the answer may be yes on the surface, we’re not asking the most important questions: ‘How well is it performing? Does it positively impact patient care? Does it increase efficiency or decrease cost?’”

Shah, speaking in the Journal of the American Medical Association (JAMA) discussed in more detail the needs of the medical field in harnessing LLMs. Concerns range from evaluating algorithm accuracy to ensuring the LLMs fit with the workflow.

He also mentions the importance of knowing who made the LLM, which data it was trained on, and whether the data applies to multiple populations. Addressing AI hallucinations and understanding mistakes are of high priority because “in health care, you can’t afford factual errors.”

LLMs present a significant opportunity to transform medical education, and practices to enhance diagnostic accuracy.  Ensuring reliability and precision in LLM-generated outputs is critical because errors can lead to incorrect diagnoses, the wrong treatment, or negative outcomes. Rigorous validation processes, constant monitoring, and working closely with experts are essential. Medical professionals should have enough medical knowledge to understand and explain the reasoning underlying LLM-generated results.

Integrating large language models into healthcare is also accompanied by investment and cost implications. Developing, training data, and maintaining LLMs demand robust computational resources, which may prevent optimal adoption, especially for those with lower available funding. Developing efficient models that require less computational power while concurrently performing at a high level is ideal, of course.

Relying on the cloud can help providers access AI capabilities and bring AI and Automation to healthcare without costly hardware and infrastructure investment. Public and private sector collaboration will also help improve quality healthcare outcomes for diverse, high-needs populations. Book a free custom AI demo for healthcare and clinical settings today!