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Large language models are radically transforming the healthcare industry, especially in the last couple of years. Combining artificial intelligence and natural language processing creates powerful models that understand human language and even visual data. With such help, healthcare institutions can unlock new insights, streamline work, and improve work with patients.
LLMs' biggest strength in the healthcare industry is the ability to comprehend data it’s trained on, and understand text, patterns, and algorithms. This leads to a wide range of available applications in the healthcare sector, such as personalized patient interactions, extraction of valuable information, and medical research support.
We’ll explore various large language models in the healthcare industry and their applications to show you the potential benefits.
Key Takeaways
- Large language models are already transforming the healthcare industry.
- Traditional automation solutions can’t keep up with LLMs.
- LLMs can continue to learn and improve even after deployment.
- Although LLMs come with certain risks, the benefits outweigh them.
- Setting up a custom LLM within your company helps save time and money.
How are LLMs Used in Healthcare?
Today, LLMs are mostly used for the following purposes in healthcare:
- Streamlining administrative tasks
- Improving patient engagement and support
- Enhancing clinical decisions
- Speeding up the advanced drug discovery research
- Providing medical education and training
- Obtaining and analyzing patient history data
These are the most common ways LLMs provide versatile and practical help in the healthcare industry. This enables healthcare companies to streamline operations, enhance the quality of the provided care, and have highly versatile practical applications.
1. Streamlining Administrative Tasks and Customer Support
Time-consuming tasks like billing, scheduling, and claim processing are now easily automated with LLMs.
Appointment scheduling, for example, is one of the areas that can make or break a company’s system and operations with clients. By automating it, staff has more time to focus on more important tasks while minimizing human error.
Besides appointment scheduling, LLMs provide patients with follow-up care instructions, can handle patient queries just as well as humans, and more. NHS in England is a great example. NHS uses an AI chatbot to streamline mental health referrals while reducing administrative burden and boosting staff capacity.
2. Enhancing Clinical Decisions
Clinicians also have to regularly make quick decisions, and LLMs can help with this as well.
They can analyze patients’ health records, clinical notes, laboratory results, and imaging studies to make informed, data-based decisions.
With such powerful analytical ability, LLMs can suggest appropriate treatment plans and potential diagnoses. As a result of such LLM applications, patients are treated more effectively.
3. Helping With Advanced Drug Discovery Research
By understanding advanced data and being able to learn further after implementation, LLMs can speed up numerous research processes, including drug discovery.
Scientific literature is growing at a rapid pace. LLMs can help analyze and interpret it to provide researchers with deep, valuable insights more quickly.
Besides helping with the research for drug discovery, LLMs can also find potential treatment candidates at a much quicker pace than traditional methods.
4. Creating Medical Education Content and Training
Being able to analyze and understand the context of medical literature, LLMs can also create up-to-date educational training and content, including simulations. This is super useful in combination with case study analysis as LLMs generate content using the latest medical research and information.
LLMs can also serve for better analysis of patients’ history data to provide treatment suggestions, follow-up care instructions, and more.
LLMs vs. Traditional Solutions
Comparing LLMs to traditional solutions shows how much the healthcare industry is advancing. The biggest differences are noticeable in the following ways:
- Data processing and analysis
- Scalability and adaptability
- Patient engagement and interaction
- Accuracy and reliability
The biggest difference between LLMs and traditional healthcare solutions lies in the data processing and analysis. While traditional healthcare solutions rely on prefined algorithms and structured data that help provide processing and analysis, LLMs are much more advanced natural language processing models.
With LLMs’ ability to understand and process human language, they can process the same data traditional solutions can, only much faster and more accurately. On top of that, LLMs can provide more comprehensive insights after processing unstructured data, as well as synthesizing information from a variety of sources, such as medical literature, patient records, case studies, research data, and more.
This is something traditional solutions can’t keep up with. LLMs can even provide treatment recommendations and assist in diagnosis, which also helps healthcare workers.
When it comes down to scalability, traditional healthcare solutions require a much bigger infrastructure, which involves more human resources. This can be cost-effective and time-consuming compared to LLMs. LLMs easily process large amounts of data, expanding the knowledge and learning from the moment it’s implemented with a very minimal need for human intervention. Therefore, LLMs can evolve at a rapid pace compared to traditional healthcare solutions.
This leads to much better accuracy of LLMs due to their impressive capabilities of understanding medical information after their training on custom data. Lastly, LLMs unlock the limits on patient interaction and can even make it feel much more personal by moving away from traditional standard forms and questionnaires.
How Will LLMs Affect Healthcare?
LLMs are already of huge importance in the healthcare industry as they’re already making changes and improving things. Compared to traditional solutions, LLMs affect healthcare in much more beneficial ways:
- Revolution of medical diagnostics
- Improved treatment plan suggestions
- Personalized patient care
- Streamlined administrative work
- Improving medical training
- Addressing challenges such as rising costs of healthcare services, financial challenges, and shortage of healthcare professionals
While these are some of the things that LLMs already contribute to the healthcare space, the greatest thing about LLMs is that they continue to improve based on the data they receive.
LLMs will continue to shape the healthcare industry, provide more benefits, and become able to handle many more tasks. According to Forbes, LLMs are already affecting the way the healthcare industry manages billing and finance. That’s just one of the rising effects of the LLMs at the time of writing. We believe that the ways to diagnose, treat, and manage patients will continue to revolutionize since the technology is already there and is making a huge impact and progress at the same time.
What Are the Risks of Large Language Models in Healthcare?
The biggest risks of large language models in healthcare are related to data privacy, security, and potential biases in the AI algorithms. With LLMs being able to understand human language and context, as well as to make predictions and recommendations, there’s also a concern that there could be challenges in the explainability of their decisions.
Certain healthcare settings are sensitive and the application of LLMs also has certain ethical implications. Luckily, all of these things can be managed and addressed by keeping things as transparent as possible, data governance, and monitoring by the human.
How Do You Assess Large Language Models?
Large language models are assessed by measuring the performance of how successful an LLM is in interpreting human language and providing accurate responses with a variety of algorithms. In such a way, LLMs are tested for comprehension, extraction of data, and the quality of text or actions produced by the LLM.
Performance and accuracy are high on the assessment list, but ethical considerations, bias, and safety & security are also of huge importance in the assessment phase. Task-specific performance, generalization ability, and robustness are some of the measured factors of performance.
When it comes down to ethical and safety assessments, things such as bias detection, fairness, and transparency are closely looked at. This is followed by misuse potential and security vulnerabilities before the LLM gets implemented.
Additionally, scalability, efficiency, and integration are assessed through factors such as interoperability with the existing system, customization, adaptability, ability to update, and regulatory compliance.
How Does an LLM Learn?
An LLM learns from training data, which can be broad and general or specific to a particular company or industry. Best results are usually achieved when LLMs learn from broad training data first and are then fine-tuned on industry- or company-specific data.
- Broad data helps them understand patterns, grammar, and context.
- Company and industry-specific data (e.g., electronic health record data) helps them understand resource requirements and generate relevant text within the right context.
LLM training can be split into 3 basic steps::
- Data collection and processing
- Pre-training
- Fine-tuning
The first step is to collect data from a variety of sources, such as books, websites, articles, and other forms of literature. This helps cover the wide range of knowledge a human can achieve. Processing helps clean the collected data and only keeps relevant information by removing inconsistencies or even protecting personal information in the previously collected data.
Pre-training is the first learning phase where models learn how to make predictive generations and learn the patterns of data. Lastly, fine-tuning helps models learn how to perform task-specific outputs and continue to learn by further adjustments.
LLMs can understand natural language like humans. Some LLMs – called multimodal models – can also interpret and learn from other data formats, like images. In combination with the ability to process large amounts of data, an LLM can quickly go through large amounts of collected data for processing. The processing helps clean the data and remove irrelevant content.
After that, the LLM is in the pre-training phase where it learns from the processed data. The learning process is all about having the LLM understand the context of the data within the given parameters of the structure. Some LLMs can continue to learn from collected data.
What Is the Future of Large Language Models?
In the future, large language models will likely continue getting bigger and more complex. Some of the most complex LLMs currently have trillions of parameters, which results in remarkable abilities.
We believe that the future of LLMs brings more efficient training, better deployments, a much more personalized touch, better contextualization, tighter integrations, and improved regulatory and governance frameworks.
With the improvements of the existing capabilities, LLMs will continue to grow and as a result benefit from broader capabilities and better integrations.
Integration of Large Language Models in Healthcare
We already see plenty of real-world applications of LLMs in the healthcare industry and the benefits are astonishing. LLMs are surely changing the healthcare sector by understanding medical data and supporting healthcare professionals in all sorts of tasks in the industry.
The future of the large language models in healthcare is promising and it’s already a path to more efficient, personalized, and accessible medical services and solutions.
If you’d like to acquire some of these benefits and save money by improving the efficiency of your workflow, please schedule a 30-minute call with our experts. You can discuss your needs and the best use cases for your company, explore and see how our LLM works live, and get help and answers to any LLM-related questions you might have.
