Revolutionary Applications of Large Language Models (LLMs) in MedTech

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One of the major areas in which technology has become a critical component is in the healthcare industry. Today’s technology has become an integral part of health care and is expected to be a powerful force in future health care delivery. As we seek to ensure a high-quality outcome and processes for the patients, technology is now contributing to the improvement of the quality of services rendered to patients and the efficiency of healthcare processes.

In 2024, the total worldwide medical technology revenue is expected to be nearly USD 600 billion.

This article presents six revolutionary uses of the Large Language Model (LLM) in Medical Technology (MedTech).

Enhanced Clinical Decision Support

Imagine a world where doctors can easily access a vast database of medical information before making a medical decision. When it comes to the highly technical healthcare industry, LLMs play the role of professional consultants. They sort through enormous amounts of medical literature, research papers, and patient data to offer the best recommendations for diagnosis, treatment, and pharmaceuticals.

Technical Insights

Some of the current LLMs include GPT-4, GPT-3. 5 Turbo, LLaMA, and Med-PaLM 2, which can be enriched by incorporating CPGs (Clinical Practice Guidelines). Three methods stand out:

• Binary Decision Tree (BDT): A more formalized approach that involves the use of CPGs along with LLMs, which enhances the ability to provide precise recommendations.
• Program-Aided Graph Construction (PAGC): Optimizing the use of graph-based representation for context-aware decision-making.
• Chain-of-Thought-Few-Shot Prompting (CoT-FSP): An approach that can reason beyond simple rules by taking into account context and a few-shot examples of the problem at hand.

For instance, LLMs with CPGs are more effective in giving recommendations that are supported by research and literature on COVID-19 outpatient management. These models are better than the simpler plain LLMs with Zero-Shot Prompting (ZSP). Clinicians gain from receiving correct information on the treatment to be followed, monitoring procedures, and follow-up.

Real-life Examples

• Ada Health: Ada’s AI-driven health companion uses LLMs to perform context-rich health checks and provide tailored advice. The patients get advised based on the best practices and possible outcomes, which improves their self-management and decision-making process.
• PathAI: Pathologists can use LLM-powered algorithms to detect cancer from histopathology slides. These models help analyze complicated medical images and enhance precision and speed.
• Zebra Medical Vision: In particular, medical imaging early disease diagnostic solutions are based on LLMs. Explaining from osteoporosis to various liver situations, Zebra’s algorithms improve the operations of radiologists.

Challenges

• Accuracy and Reliability: Since LLMs offer recommendations, they must offer accurate recommendations, as incorrect advice could negatively affect the patients. This means that evaluation has to be intense, and continuous fine-tuning is essential.
• Contextual Nuances: These concepts are not enough if LLMs don’t know the context. Medical terminology, the patient’s history, and gestures influence decision-making.
• Ethical Use: It is thus paramount to ensure that LLMs adhere to ethical guidelines and avoid biases, which is critical.

LLMs are effective in detecting diseases, anticipating their progression, and recommending therapy. The capacity to handle unstructured data, including clinical notes and imaging reports, together helps in clinical decision-making. 

Accelerated Medical Research

Unlocking medical breakthroughs faster than ever. LLMs learn through extensive scientific databases, which they utilize to find hidden patterns and associations that human researchers may fail to spot. They allow for faster drug development, faster understanding of genetic information, and faster epidemiology research.

Technical Insights 

BioBERT, ClinicalBERT, and BlueBERT are particular LLMs that are pre-trained for biomedical use. These models excel at:

• Entity Recognition: Identifying specific entities like genes, proteins, etc., from the research articles.
• Relationship Extraction: Identifying relations between the medical concepts to be taught.
• Natural Language Inference: To respond to multiple questions that involve diseases, treatments, and underlying processes.

Such LLMs like ChatGPT are capable of easily summarizing research papers and pinpointing the most essential findings. Users can easily find specific details of a particular topic in a shorter time than while reading the whole content.

Real-life Examples

• BioBERT: is a pre-trained model that is fine-tuned on a large amount of biomedical data based on BERT. It performs well in terms of entity recognition, relation extraction, and response to biomedical questions.
• ClinicalBERT: Especially for clinical data, ClinicalBERT enhances performance in tasks such as predicting patient survival and anonymizing clinical data from electronic health records.
• BlueBERT: Established on BERT, this BlueBERT is precise in different biomedical NLP tasks to help researchers decipher complicated texts.

Challenges

• Data Quality and Bias: As stated earlier, LLMs work with past data, which sometimes may have bias or inaccuracies. To this end, it is essential to maintain high-quality training data.
• Ethical Use: It is crucial to consider the ethical aspects while developing the LLM capabilities and making decisions. Abuse can result in wrong assumptions or even have adverse effects on the patients.

Through big data analysis, LLMs enable medical research by sorting through large volumes of unstructured data, identifying patterns, and expediting the discovery of new information. By analyzing genomic data, identifying potential drug targets, and predicting drug interactions, LLMs streamline the process, helping patients get the best medicine treatment for their individual needs.

Improved Patient Engagement

Empowering patients with knowledge.  LLMs produce easy-to-understand patient elements that deconstruct medical jargon, educating them on the medical status and insights. They improve the way healthcare providers and patients communicate.

Benefits

• Improved patient experience and satisfaction: Chatbots powered by the LLM ensure that patients engage in meaningful, easy, and convenient interactions that improve their experience.
• Increased efficiency: Some of these chatbots operate in the capacity of doctors or nurses, helping in tasks such as setting appointments or reminding patients of their drug dosages, thus saving the time of these professionals.
• Cost-effectiveness: Round-the-clock support does not require extra human resources; hence, its implementation does not create pressure on the company to employ more people.
• Accessible healthcare: Chatbots can use pre-set questions and responses to educate various demographics, especially those with little to no access to healthcare services.
• Personalized care: Using data collected from patients, chatbots provide personalized approaches to medication and its administration and give notifications.
• Improved patient outcomes: They support the treatment of long-term illness and enhance the use of prescription drugs.

Real-life Examples

Some use cases of ChatGPT have revealed that it can be incorporated into the development of chatbots capable of performing complete disease diagnosis processes for patients.

• Woebot Health has developed efficient and effective digital mental health solutions with the use of artificial intelligence. Their Woebot Health Platform is a chat application-based mental health support for patients and members that is immediately available. It can augment patient care, advance the health of the population, and optimize the work of the providers. The platform increases speed for support, gathers PROs, and ensures constant availability without overburdening clinicians. Also, it captures reimbursement data for PRO and serves as an in-network referral to eliminate the leakage.
• Florence Healthcare provides clinical trials with a continuous, remote environment for working on trials. Their Site Enablement Platform optimizes processes, allows for remote site monitoring, and centralizes study processes to sites. In doing so, Florence ensures that sites’ needs are addressed and, as a result, supports enhanced collaboration between sponsors and sites, which leads to trials being unburdened and advanced. Florence has been adopted by over 18,000 research teams in more than 55 countries and organizes over 5.2 million research workflows on a monthly basis.
• GYANT is a digital health company that offers artificial intelligence-based virtual assistants to health systems. Their Intelligent Care Enablement system, Fabric, was designed to improve the healthcare experience through convenience. For example, GYANT’s virtual assistant can assist patients in monitoring symptoms, identifying clinics or doctors within a health system, or making an appointment.

Challenges

• Quality control: To guarantee that the information being developed by the LLMs is correct and credible.
• Ethical considerations: Regarding the balance between the protection of personal information and the principle of consent, transparency is also one of the major principles of the GDPR.
• Integration into existing systems: The integration with LLM-powered solutions in a fluent manner.
• Health literacy: The issue of making sure that patients fully understand what is in LLM-generated content.
• Bias and fairness: Steps taken in LLM to overcome biases present in training data.

Patients being educated on their diseases ensures that they comply with treatment, and therefore, their health improves. LLMs are the best way to reduce the communication barrier.

Streamlined Health Data Management

Automating administrative tasks for efficiency. The LLM chatbots address the questions related to inquiries, appointments, and insurance. They improve communication and facilitate information flows as well as improve patient experience. Employees no longer have to spend time talking to customers, and LLMs make the workflow quicker.

Using large language models to improve administrative functions in healthcare is helpful within the framework of Streamlined Health Data Management.

• Claims Processing Automation: LLMs can also more efficiently process and validate insurance cases. For example, they can pull information from the patient’s records, compare it with the insurance plans, and process the claims themselves.
• Appointment Scheduling Optimization: In fact, LLMs can easily schedule appointments by considering the patient’s choice, the doctor’s time, and the clinic’s capacity. They can recommend the most suitable time slots for appointments and also rearrange schedules.
• Insurance Documentation Assistance: From the above discussion, it emerges that LLMs help in responding to insurance queries, explaining policies, and providing documents. It allows them to issue accurate information to their policyholders and minimize the need for customer care services.  

Real-life Examples

• DRUID AI uses Conversational AI for Healthcare and helps to engage patients and optimize processes. With over 500 ready-made templates, the platform includes features such as patient enrollment, appointment setting, health status tracking, billing, and inventory/claims management.
• MedWhat is a virtual assistant that can give appropriate and verified information on different health issues to both consumers and doctors. The answers are generated by an intelligent system, which is a super-computer that learns about medicine, health records, and medical questions. MedWhat’s approach is based on the concepts of big data and using data science on the information that is stored in the 2D and 3D medical images, EHR, and Wearable devices. From a patient’s point of view, MedWhat provides a simple application that provides not only answers but also reminders, follow-ups of wellness, and one place to manage incoming data from wearable sensors and other sources.

Challenges

• Data Privacy and Security: Extremely delicate health information must go through stringent security measures. This is why it is important for organizations to adhere to regulations such as HIPAA.
• Customization and Adaptability: Thus, it can be suggested that LLMs require further customization to the particular context of healthcare. It is difficult to build versatile models that improve their performance by adapting to real-world interactions.
• Ethical Considerations: Indeed, it is critical to find a middle ground between automation and the human approach to clients. Non-bias and fair treatment remain an issue of concern to this effect.

Image Analysis and Reporting

The eyes of an expert multiplied.  It can be seen as an amplification of an experienced medical staff’s wisdom. LLMs are used in diagnosing medical images – X-rays, MRIs, and mammograms—with absolute accuracy. They help radiologists in making clinical decisions and subsequent discussions as they have a strong capability to detect abnormalities and improve diagnostic performance.

Google’s Med-PaLM 2

Med-PaLM 2 continues previous LLM models with a focus on the medical field. It seeks to give accurate answers to a wide range of health-related queries.

• Medical Question Answering: Med-PaLM 2 achieves a 97% pass in USMLE-style questions. It provides timely and relevant responses to consumers’ health-related questions.
• Image Analysis: Med-PaLM 2 can process textual images, which include x-rays and mammograms.
• Diagnostic Reports: It also provides its users with reports about these images, which help radiologists and physicians.
• Follow-Up Dialogue: Med-PaLM 2’s second use is to facilitate discussions by allowing experts to discussthe results.
• Achievements: Med-PaLM 2 performed at the level of a human expert in answering questions similar to the USMLE and scored 86. This model achieves a 5% accuracy on the MedQA medical exam benchmark.

The identified challenges to be overcome are specific to utilizing AI tools in this field: specific issues related to data privacy, customization, and the ethical use of such models to create impactful medical AI systems.

Real-life Examples

Dermatology

Dermatology images are classified by AI models that help diagnose skin cancer, especially the melanoma type, which is often fatal. These models employ features like color, texture, and patterns that may not be distinguishable to the human eye but are characteristic of skin cancer. Artificial neural networks are used in melanoma detection. They are highly sensitive and provide better results than conventional diagnosis procedures. Some limitations include data quality, interpretability, and ethical concerns, but future developments seek to incorporate AI into clinical workflows for precision medicine.

• Nevisense is a medical device developed for the assessment and diagnosis of skin cancer at an early stage. It employs electrical impedance spectroscopy (EIS) to determine the electrical characteristics of skin tissue. Thus, Nevisense is beneficial for melanoma and other skin lesions as it helps to observe minor deviations, making the diagnostic process more accurate and efficient for the patient.
• Sklip is a device that has the potential to prevent skin cancer in a very innovative way. For instance, there is a smartphone attachment called Sklip®, which is used as a dermatoscopy. When placed adjacent to the screen of a phone that has a camera, it takes clear pictures of moles, through which users can determine whether or not the skin growth represents cancer. Another function of the Sklip app is to link users to dermatologists for learning purposes.
• MetaOptima is a Canadian corporation that specializes in digital health and, specifically, intelligent dermatology. Their AI-based system, DERM, is designed to utilize machine learning techniques to identify malignant, pre-malignant, and benign skin lesions such as melanoma. The aim is to make dermatologist quality assessments more available while offloading more work from healthcare systems worldwide.
• Skin Analytics is a mobile application that implements artificial intelligence to diagnose skin cancer. It has a capability called DERM that can identify different types of lesions: malignant, pre-malignant, and benign. Skin Analytics can make early diagnosis or discharge decisions for patients by analyzing skin lesion images, leading to better patient care and reduced health system costs.

Retina Imaging

Computer programs identify diabetic retinopathy and other diseases affecting the eye from images of the retina. This involves the use of machine learning algorithms on the retinal image to determine DR and DME.These algorithms detect diseases, estimate the course of diseases, and evaluate possible outcomes of treatment.

Radiology (3D and 2D)

AI categorizes and detects abnormalities in medical images. It is also used to identify abnormalities in X-rays, CT scans, and MRIs. In the example of chest X-rays, AI offers promising results in normal and abnormal case identification, thus easing the burden on radiologists.  It is also revealed that deep learning models can perform more accurately in diagnosing from plain radiographs, CT scans, and MRI scans.

Pathology

Image analysis assists pathologists in determining which cells in the tissue sample are cancerous by categorizing the tissue samples in terms of the presence of cancerous cells. Machine learning techniques, especially natural language processing, are used to identify key features from large text data of medical reports to diagnose cancer types, grades, and other characteristics. These are mainly data limitations, data validation, and the integration of the proposed system with the existing healthcare information technology systems.

Health Records

Using image data to pull out specific details from the medical records. On the extraction of information from medical documents
Electronically generated medical records are created by digitizing paper-based medical records using OCR (optical character recognition). When implemented, OCR transforms documents and helps systems identify and retrieve patient information, history, and test results from the scanned documents to provide better patient care.

Risk Management in MedTech

Navigating risk with AI precision. ISO standards help in improving the safety and efficacy of medical devices with the help of LLMs. They calculate risk factors, forecast adverse outcomes, and improve risk management strategies.

Companies align AI/ML-based risk management with ISO 14971:2019 and ISO 13485:2016 (Quality Management System for Medical Devices). Additionally, the FDA provides guidance on filing 510(k) for SaMD devices and handling modifications. The recently introduced Technical Information Report (TIR) 34971 bridges AI/ML risk management with ISO standards.

ISO 14971:2019

It offers a structured procedure for risk evaluation and risk management of medical devices, and SaMD in particular. The process starts by establishing the possible risks or hazards that are likely to arise when using the device or engaging in a particular process. It estimates the probability and consequence of the adverse outcome and integrates the two measurements to evaluate the risk. After measures to minimize or avoid risks are implemented, it assesses the adequacy of the risk management strategies.

Some of the challenges that arise with the use of Artificial Intelligence/ Machine Learning in risk management include:

• Rapid Development Cycles: Software is somewhat dynamic, and this implies that the risk management processes must also be dynamic.
• Data-Driven Risks: This risk involves AI/ML models that depend on data and quality; bias and privacy can affect data.
• Model Interpretability: Another concern with AI/ML models is that its decision-making process is not easily understandable.
• Adaptive Systems: AI/ML systems learn and evolve, making risk evaluation a continuous process.

ISO 13485:2016

It is the harmonized standard for quality management systems for medical devices. ISO 13485:2016 focuses on the quality of medical devices throughout their lifecycle: design, development, production, purchase, storage, installation, servicing, and final withdrawal from use. It is the latest standard that outlines the current QMS requirement for medical device businesses and fills this gap as it offers very useful and clear recommendations on how to implement QMS. By following this standard, the companies ensure the safety of patients and observe high standards in their operations.

Successful Implementations and Solutions

Several real-life examples demonstrate effective AI/ML-based risk management in MedTech:

• Diagnostic Imaging: They complement diagnostic procedures by using artificial intelligence to parse image data, including X-rays and MRIs. They serve to improve the diagnostic precision in relation to potential benefits and harms from false-negative/positive results.
• Predictive Analytics: Current AI models forecast the decline of a patient’s health and thus can be attended to on time. Risk management is another step in the process that entails the confirmation of the model performance and handling of false signals.
• Drug Safety: AI tracks adverse reactions to a specific drug and keeps a record of the risks associated with the drug. The ability to maintain data quality and address biases is important.
• Clinical Decision Support: AI helps clinicians make decisions regarding the actions to be taken in treating patients. Uncertainties are confirmed, and recommendations are validated in risk management.

MedTech Breakthroughs with LLM

In conclusion, LLMs are transforming the MedTech industry by advancing clinical decision support, facilitating medical research, enhancing patient engagement, optimizing health data management, supporting image studies and reporting, and facilitating risk management. While these applications are developing rapidly with the help of LLMs, the future of healthcare will depend on several challenges associated with bias, explainability, and scalability.

Let’s embrace this transformative potential and build a healthier future together.