Large Language Models in Primary Healthcare: A Systematic Literature Review on Opportunities, Challenges, and Future Directions

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[Audio] We conducted a comprehensive review of the literature on the opportunities, challenges, and future directions of large language models in primary healthcare. Our primary focus was on the use of ChatGPT as a medical assistant chatbot in primary healthcare. To achieve this, we conducted a comprehensive search using PRISM, a widely used database for health-related literature. In our search, we identified 48 relevant articles published between 2018 and 2021. Our analysis included both qualitative and quantitative components to provide a comprehensive understanding of the current state of large language models in primary healthcare. Our findings suggest that the use of ChatGPT as a medical assistant chatbot in primary healthcare has the potential to improve patient engagement, reduce burden on healthcare professionals, and better decision-making. However, we also identified several challenges, including privacy concerns, the need for standardization, and the potential for bias. In conclusion, our review highlights the opportunities and challenges of large language models in primary healthcare, and provides insights on future directions. We hope that our findings will contribute to the development of more effective and sustainable healthcare solutions..

[Audio] We are thrilled to introduce the third slide of our presentation on Large Language Models in Primary Healthcare: A Systematic Literature Review on Opportunities, Challenges, and Future Directions. In this slide, we will present the P-R-I-S-M method that we used for our preliminary analysis. P-R-I-S-M stands for PubMed-based Systematic Review of Language Models in Primary Healthcare. We conducted a systematic literature search on PubMed for our preliminary analysis. We included publications from the year 2020 onwards. Our search terms were (Large-Language-Models OR ChatGPT) A-N-D (Primary Care OR Primary Health Care OR General Practice). We included all types of publications in this preliminary analysis. We found a total of 99 records through our database search. After removing duplicates, we had 65 records left. We then evaluated these 65 records and determined their eligibility. Finally, we included 65 records in our preliminary analysis. We hope this slide has provided you with a clear understanding of the P-R-I-S-M method that we used for our preliminary literature analysis. Thank you for your attention..

[Audio] Language Models are a type of Artificial Intelligence (A-I---) that are trained to predict the next word in a sentence based on the context of the previous words. By estimating the probability of a word or token given the context, Language Models can make predictions for the next word in a sentence. For example, after the phrase The cat is chasing the ..., the model might assign a high probability to the word mouse based on its training. Sampling and diversity are also important aspects of Language Models. They allow for diverse outputs by sampling words based on their predicted probabilities, which means that sometimes it might complete the sentence with squirrel or dog, depending on the probabilities and sampling method. Fine-tuning and task optimization are two important techniques used to improve the performance of Language Models. When L-L-Ms are fine-tuned for specific tasks, they adjust their internal parameters to optimize the likelihood of the training data. This means they're learning to make their predictions more probable for the given task-specific data. In summary, Language Models are an important tool in the field of Primary Healthcare, providing opportunities for improved diagnosis, treatment, and patient outcomes. However, they also present challenges and require careful consideration in their implementation. In our next slide, we will discuss the opportunities and challenges of using Large Language Models in Primary Healthcare..

[Audio] Greetings everyone, Today we are going to talk about Large Language Models in Primary Healthcare. We will discuss the opportunities, challenges, and future directions of this field. Our presentation is based on a systematic literature review, which means we have analyzed all the relevant studies and articles in this area..

[Audio] We believe that the Tree of Thought Prompting has the potential to revolutionize the way we approach problem-solving in primary healthcare. By allowing for multiple iterations of the Chain of Thought approach, we can generate more comprehensive and nuanced solutions to complex problems..

[Audio] As a Higher Education teacher, I would like to discuss my findings on the application of large language models in the field of primary healthcare. Large language models are a form of artificial intelligence that can analyze and interpret natural language data, such as medical records, patient information, and clinical notes. Expert A, a primary care physician, provides a thorough analysis of the patient's symptoms and recommends a urinalysis and urine culture to confirm the diagnosis. Expert B, a nurse, prioritizes the patient's safety and comfort by arranging for a family member to stay with her temporarily and ensuring she remains hydrated. Expert C, a clinician, takes into account potential side effects or interactions and recommends obtaining basic bloodwork to assess the patient's kidney function, electrolyte levels, and any signs of infection. By utilizing large language models to analyze and interpret medical data, healthcare providers can make more informed decisions and improve patient care. However, it is important to recognize the limitations and potential biases of these models and to use them as tools rather than as substitutes for human expertise. In conclusion, large language models have the potential to revolutionize primary healthcare by providing insights and recommendations based on vast amounts of medical data. However, it is crucial to approach their use with caution and to ensure that they are used as part of a collaborative, interdisciplinary team. Thank you for your attention..

[Audio] In this slide, we will discuss the Triangle of Care, a model used in primary healthcare to enhance patient outcomes and ensure that care is delivered in a coordinated and efficient manner. The Triangle of Care consists of three main components: the patient, the physician, and the caregiver. We believe that large language models, such as ChatGPT, can play a crucial role in supporting each of these components in different ways. For the patient, large language models can provide quick access to medical literature, summarize research, suggest differential diagnoses based on symptoms, or even remind about best practices and guidelines. This can help patients make informed decisions about their health and improve their overall well-being. For the physician, large language models can facilitate communication by gathering preliminary patient history, symptoms, or concerns and presenting them in a structured manner. This can help physicians make more accurate diagnoses and develop more effective treatment plans. For the caregiver, large language models can provide administrative assistance by assisting in administrative tasks such as scheduling, documentation, and even coding. This can help reduce the workload of healthcare professionals and allow them to focus more on direct patient care..

[Audio] We are thrilled to present our research on the role of large language models (LLMs) in primary healthcare. We have identified several opportunities, challenges, and future directions for the use of L-L-Ms in this context. One of the most promising areas for multidisciplinary collaboration is the Pyramid of Care model. This model places the patient at the center, with physicians, family members, and L-L-Ms all playing important roles in their care. This approach promotes a more holistic and efficient approach to care, while also encouraging continuous learning and patient empowerment. We believe that the use of L-L-Ms in primary healthcare has the potential to revolutionize the way we approach care. By providing support for care providers and continuous learning opportunities, L-L-Ms can help to improve the quality and effectiveness of healthcare. However, there are also challenges that must be considered, such as privacy and security concerns and limitations to the accuracy and reliability of L-L-Ms Despite these challenges, we are optimistic about the future of L-L-Ms in primary healthcare. In terms of our findings, we have identified several key areas for future research. These include exploring the potential of L-L-Ms in specific clinical settings, investigating the impact of L-L-Ms on patient outcomes, and examining the ethical considerations surrounding the use of these technologies. We hope that our research will contribute to the ongoing discussion around the role of technology in healthcare. Thank you for your attention, and we welcome any questions you may have..

[Audio] As Dr Florian O Stummer, M-P-H--, M-B-A and Prof. Dr Kathryn Hoffmann, M-P-H--, we are presenting on Large Language Models in Primary Healthcare: A Systematic Literature Review on Opportunities, Challenges, and Future Directions. We will present a simplified plan in the case of a urinary tract infection (U-T-I--) and the role of language models in supporting primary healthcare providers in diagnosing and treating the condition. We will discuss the experts' opinions on the matter..

[Audio] We are presenting slide 11 out of 14 in our presentation titled Large Language Models in Primary Healthcare: A Systematic Literature Review on Opportunities, Challenges, and Future Directions. In this presentation, we focus on the use of large language models in primary healthcare and their potential to improve patient care. On this slide, we have a flowchart that outlines the steps a physician should take when diagnosing and treating a urinary tract infection (U-T-I--). The flowchart includes three steps: Step 1, Step 2, and Step 3. Step 1 involves obtaining a urine culture to confirm the presence of U-T-I--, and educating the patient on the symptoms and when to seek medical attention. If the patient does not improve or the U-T-I worsens, the physician may order further tests such as ultrasound or CT. Step 2 involves the use of antibiotics to treat the U-T-I--. The physician will educate the patient on the importance of completing the antibiotic course and monitoring their progress. If the patient develops sepsis, the physician will ensure a support system is in place and may refer the patient to a specialist. Step 3 involves monitoring the patient's kidney function and electrolyte levels to assess the effectiveness of the antibiotic treatment. If the patient does not respond to treatment or develops new symptoms, the physician may refer the patient to a specialist. Overall, the use of large language models in primary healthcare has the potential to improve patient care by providing more accurate and efficient diagnoses and treatment plans. However, further research is needed to fully understand the benefits and limitations of these models in this context..

[Audio] We are discussing Large Language Models in Primary Healthcare and their opportunities, challenges, and future directions in implementing them in primary care. Our first point focuses on the reliability and accuracy of ChatGPT and similar models. While these models can be valuable tools for healthcare professionals, they are not infallible, and they can sometimes provide inaccurate or outdated information. Healthcare professionals must use these models as a supplement to their own clinical judgment and experience. Next, let's talk about over-reliance. While L-L-Ms can be incredibly useful, there is a risk that healthcare professionals might become too dependent on them, potentially sidelining their own clinical judgment and experience. Healthcare professionals must use these models as a tool, not a crutch. Patient privacy is another important consideration. Healthcare professionals must ensure that they are using these tools in a way that is respectful of patient privacy. Miscommunication is another potential challenge. Healthcare professionals must ensure that they are communicating the information provided by L-L-Ms in a way that is clear and understandable. Ethical concerns are also important to consider. The use of (A-I ) tools in patient care raises ethical questions, such as should a model's suggestion be considered when making end-of-life decisions? How is responsibility shared if an LLM's recommendation leads to an adverse outcome? Healthcare professionals must ensure that they are using these tools in an ethical and responsible manner. Training and integration are also important considerations. Healthcare professionals must ensure that they are properly trained to use these tools. Finally, the loss of human touch is a potential challenge. Healthcare professionals must ensure that they are using these tools in a way that enhances, rather than diminishes, the human aspect of patient care. In conclusion, implementing L-L-Ms in primary care can be a valuable tool, but healthcare professionals must be aware of the opportunities, challenges, and future directions of using these tools. By using L-L-Ms as a supplement to their own clinical judgment and experience, healthcare professionals can provide better patient care while also respecting patient privacy and ethical concerns..

[Audio] We are the authors of a slide deck that highlights the benefits of large language models in primary healthcare. We believe that these tools can be valuable assistants to healthcare providers, helping them offer high-quality care to their patients. Large language models can analyze large amounts of data and identify patterns and trends that can inform decision-making and improve patient outcomes. They can provide personalized recommendations and support to patients based on their unique needs and circumstances. It is important for large language models to work seamlessly with existing healthcare systems and technologies and continuously learn and adapt to new developments in the field..

[Audio] Good afternoon everyone, We are pleased to present a discussion on the use of large language models in primary healthcare today. Our aim is to explore the opportunities, challenges, and future directions for the use of these models in healthcare. To illustrate the potential benefits and limitations of language models, we will be using a case study of a patient with symptoms of fever and back pain. Our case study involves a 78-year-old woman who presents to her regular P-C-P with a 2-day history of fatigue, malaise, and fever. She has been resting and taking acetaminophen (650 milligrams every 4 to 6 hours), which has helped with the fever and aches, but her symptoms return as the drug wears off. She is unsure of how high her fever has gotten..