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Large Language Models in Healthcare: Examples & 10 Use Cases in 2024

Written by
Cem Dilmegani
Cem Dilmegani
Cem Dilmegani

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

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The healthcare industry, full of vast amounts of patient data and medical literature, seeks efficient ways to use this information for better patient outcomes. Traditional methods of data analysis and manual interpretation are time-consuming and often lag behind the rapid pace of medical advancements, potentially compromising patient care.

By deeply leveraging the capabilities of large language models in healthcare processes, healthcare organizations can provide better patient care, research, and data privacy. Their ability to understand, generate, and summarize text-rich data ensures that healthcare remains informed, efficient, and ethical.

In this article, we will explain:

  • Current studies for leveraging large language models in healthcare
  • 10 use cases of large language models in healthcare
  • Challenges of large language models in healthcare

What are the studies for leveraging large language models in healthcare?

LLMs are general language models trained on vast amounts of data on web services. Therefore, they are not selective nor specialized. For a specific application area, LLMs must be fine-tuned with the data within that area, such as literature, or healthcare.

For the fine-tuning and training of LLMs, you can check our in depth articles.

Currently, although not used widely, there are attempts to use large language models in healthcare and medical applications through fine-tuning. 

1- BioBERT

BioBERT, a specialized language model for biomedicine derived from the BERT framework, has undergone further refinement using extensive biomedical datasets, encompassing PubMed summaries and PMC comprehensive articles.1 This enhancement has resulted in notable progress in biomedical natural language processing activities, including:

  • Pinpointing specific entities
  • Discerning relationships
  • Addressing queries

Figure 1. Overview of the pre-training and fine-tuning of BioBERT

BioBERT is an example of the use of large language models in healthcare

2- ClinicalBERT

ClinicalBERT, a specialized model tailored for the clinical domain, has been further refined using the MIMIC-III dataset, containing electronic health records from intensive care unit patients.2 This adaptation has led to improved outcomes in clinical natural language processing functions such as:

  • Forecasting patient survival rates
  • Data anonymization
  • Diagnostic categorization.

3- BlueBERT

BlueBERT, also founded on the BERT structure and trained with an extensive collection of biomedical textual information, has reached high efficiency in diverse biomedical natural language processing attempts.3 This includes identifying specific entities, understanding relationships, and responding to biomedical queries.

10 Use Cases of Large Language Models in Healthcare

1- Medical Transcription

One of the most promising use cases of large language models in healthcare is medical transcription. Medical transcription, the procedure of converting spoken medical observations into written health records, is time-consuming and prone to errors due to human evaluation. AI medical transcription utilizes machine learning and natural language processing (NLP) abilities of LLMs to: 

  • Listen to the organic dialogue between a patient and clinician
  • Extract important medical details
  • Condense that medical data into compliant medical records that align with the relevant sections of an EHR

By doing this, artificial intelligence (AI) technology, through LLMs, can automate the medical transcription process with cost and time savings, increased accuracy and better outcomes.

2- Electronic Health Records (EHR) Enhancement

The proliferation of electronic health records (EHR) has accumulated a vast repository of patient data, which, if mined effectively, can become a goldmine for healthcare improvement. LLMs are exceptionally skilled at interpreting vast and complex datasets like electronic health records. Through advanced natural language processing capabilities, they can sift through clinical notes written by healthcare providers, making sense of various patient narratives.

By doing this, they can structure and categorize patient data more efficiently. As a result, medical professionals can quickly identify patterns or anomalies, reducing the chances of oversight and leading to improved patient care.

3- Clinical Decision Support

In the realm of medical practice, swift and accurate clinical decision making is paramount. Large language models in healthcare can serve as invaluable assistants, scouring vast textual patient records and relevant medical literature. Their capacity to comprehend and summarize complex medical concepts allows them to offer valuable insights to human medical professionals.

This not only helps healthcare professionals in arriving at informed decisions but also ensures a comprehensive review of available data, thus enhancing patient outcomes.

4- Medical Research Assistance

The ever-expanding body of medical research can be daunting for healthcare professionals to keep up with. LLMs, with their ability to parse and summarize vast amounts of data, can extract key findings from new research, providing synthesized insights. For example, one of the most famous LLMs, ChatGPT, is used for text summarization. This and similar tools can be used for summarizing extended medical research papers.

This means that healthcare professionals can stay updated with the latest advancements without being overwhelmed, ensuring that patient care remains at the forefront of medical innovation.

5- Automated Patient Communication

Effective communication is crucial in healthcare. Sometimes patients prefer only talking and asking questions to a medical professional for their simple symptoms without having an appointment. Sometimes they want to make an appointment after making their minds about the symptoms or the disease. That’s why healthcare chatbots are important.

LLMs are the underlying technology behind interactive and intelligent chatbots. Large language models in healthcare can draft informative and compassionate responses to patients’ queries using their natural language understanding and generation power. By offering insights into conditions, addressing FAQs, or providing medication guidelines, they can enhance the patient experience. This not only builds trust but also ensures that patients have a clear understanding of their health situation.

6- Predictive Health Outcomes

Prevention is often better than cure. In the realm of healthcare, predictive analysis offers a glimpse into potential future health challenges. While primarily text-oriented, LLMs have the potential to assist in predictive analysis by discerning patterns within textual patient data. By evaluating extensive patient histories and related notes, they can spotlight potential health risks or patterns.

This proactive approach can be invaluable to healthcare providers, offering them an additional tool to anticipate and mitigate potential health issues, leading to more proactive and preventive care.

7- Personalized Treatment Plans

Personalization is becoming central to modern healthcare. LLMs, by scrutinizing textual patient records, can draft or suggest treatment plans tailored to an individual’s medical history and specific needs. Their ability to distill complex patient narratives into actionable insights can ensure that each patient receives a care plan that’s as unique as their health journey.

8- Medical Coding and Billing

Behind the scenes of patient care lies the intricate world of medical coding and billing—an area where precision is crucial. Mistakes here can lead to financial discrepancies or medico-legal issues. Large language models offer a solution by automating these audit processes. By analyzing the specifics from patient records and EHRs, they can generate accurate codes, reducing the margin of error and enhancing the efficiency of the administrative process.

9- Training and Education

Medicine’s complex and ever-updating knowledge base is a challenge for both budding and seasoned medical professionals. Large language models and generative AI in general can be leveraged as interactive educational tools, elucidating complex concepts or offering clarifications on perplexing topics. By serving as a supplementary resource, they ensure that medical professionals are always equipped with knowledge easily, leading to improved health outcomes.

10- Ethical and Compliance Monitoring

In a digital age, maintaining the sanctity of patient safety and data is of utmost importance. Large language models in healthcare can be trained to vigilantly monitor textual data for potential ethical or privacy breaches. Whether it’s recognizing the unauthorized sharing of patient details or ensuring compliance with regulations, they can play a pivotal role in upholding the trust that patients place in healthcare institutions.

Challenges of Large Language Models in Healthcare

Accuracy and reliability

Medical decisions can be life-altering, and there’s little room for error. Large language models in healthcare, while powerful, can still produce inaccuracies or misunderstand context. A misinterpretation or incorrect recommendation could have grave consequences for patient care.

Generalization vs. specialization

Healthcare encompasses a wide range of specialties, each with its nuances. An LLM that’s trained on general medical data might not have the detailed expertise needed for specific medical specialties.

Biases and ethical considerations

Beyond accuracy, there are ethical concerns, like the potential for LLMs to perpetuate biases present in the training data. This could result in unequal care recommendations for different demographic groups.

For more detail on the challenges of large language models in healthcare, you can check our articles on the risks of generative AI and ethical considerations around it.

If you have questions about large language models in healthcare or need help in finding vendors, we can help:

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Cem Dilmegani
Principal Analyst

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Cem's hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are, Washington Post.
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Empowering AI Leadership: AI C-Suite Toolkit, World Economic Forum.
Science, Research and Innovation Performance of the EU, European Commission.
Public-sector digitization: The trillion-dollar challenge, McKinsey & Company.
Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.

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