Large language models are a type of artificial intelligence made to understand and create human language by studying large amounts of data. In healthcare, these models are used to support clinical decisions and help patients learn about their health. They do not replace doctors or nurses but assist in managing the growing amount of medical information by providing quick and useful insights.
LLMs work with medical data from many sources like electronic health records (EHRs), clinical notes, medical research, and even medical images. Being able to handle different kinds of data helps them assist in complex medical tasks. This means doctors and staff can get more accurate help with diagnosing and suggesting treatments.
The main ways LLMs are used in clinics include:
Checking how well large language models work in healthcare is not easy. Healthcare is a high-risk area where patient safety matters most. So, measuring LLM performance uses many methods like automatic scoring and reviews by human experts.
Evaluations use data from medical databases, carefully made clinical questions, and real healthcare tasks. These tests include:
Besides checking if answers are correct, tests look at reasoning and suitable use of healthcare tools. This approach helps make sure LLMs provide right information and manage medical tasks properly.
Researchers like Xiaolan Chen and Jiayang Xiang have shared important research on these tests. They point out challenges when adding LLMs to healthcare, such as false answers (called hallucinations) and the need to keep ethical standards. This research is supported by groups like the Chinese Medical Association.
Health informatics is linked to how LLMs are used in medicine. It means using technology and software to collect, save, and study health data across many care settings. It mixes nursing, data science, and analytics to make patient information useful.
In the United States, good health informatics helps hospitals and clinics by:
Studies by Mohd Javaid, Abid Haleem, and Ravi Pratap Singh explain how health informatics improves patient care and hospital operations. For healthcare managers, combining health informatics with AI models helps use resources better and keeps patients safer.
Even though LLMs and health informatics have benefits, there are problems that need solving for real use in U.S. healthcare:
Jia Li and others, connected with the Chinese Medical Association, say the future of healthcare depends on a careful mix of human skill and AI power.
One big help for healthcare managers and IT staff is the use of AI to improve workflow. This means AI handles normal front-office and clinic tasks, helping things run more smoothly.
Areas helped by AI workflow automation include:
Using AI workflow automation lowers staff costs and improves patient satisfaction with faster responses and fewer mistakes. It also helps check and improve the quality of data flowing between systems, which makes LLMs work better in clinical decisions.
Large language models, health informatics, and workflow automation bring clear benefits to health systems in the U.S. These include:
As healthcare rules get stricter and patients expect more, leaders must think about these technologies to improve care and stay competitive.
Creating safe AI tools for healthcare needs ongoing teamwork between doctors, administrators, and technology experts. This ensures AI meets real healthcare needs while keeping ethical rules and patient safety.
Researchers like Mingguang He say it is important for clinicians and AI developers to work together. They should share knowledge about medical challenges, check AI behavior with experts, and change workflows to fit new technology.
This cooperation will help improve the role of LLMs in medicine, making sure these tools support and do not replace human medical judgment.
Large language models are being used more in healthcare. They help support clinical decisions and teach patients through better data processing. Healthcare managers in the United States should understand how LLMs work with health informatics and AI automation to make care more efficient and focused on patients.
By using these tools carefully, paying attention to data quality, privacy, and ethics, healthcare providers can improve clinical accuracy, reduce paperwork, and make communication better. The key is to balance new technology with human skill to build a better healthcare system for everyone.
LLMs are primarily applied in healthcare for tasks such as clinical decision support and patient education. They help process complex medical data and can assist healthcare professionals by providing relevant medical insights and facilitating communication with patients.
LLM agents enhance clinical workflows by enabling multitask handling and multimodal processing, allowing them to integrate text, images, and other data forms to assist in complex healthcare tasks more efficiently and accurately.
Evaluations use existing medical resources like databases and records, as well as manually designed clinical questions, to robustly assess LLM capabilities across different medical scenarios and ensure relevance and accuracy.
Key scenarios include closed-ended tasks, open-ended tasks, image processing tasks, and real-world multitask situations where LLM agents operate, covering a broad spectrum of clinical applications and challenges.
Both automated metrics and human expert assessments are used. This includes accuracy-focused measures and specific agent-related dimensions like reasoning abilities and tool usage to comprehensively evaluate clinical suitability.
Challenges include managing the high-risk nature of healthcare, handling complex and sensitive medical data correctly, and preventing hallucinations or errors that could affect patient safety.
Interdisciplinary collaboration involving healthcare professionals and computer scientists ensures that LLM deployment is safe, ethical, and effective by combining clinical expertise with technical know-how.
LLM agents integrate and process multiple data types, including textual and image data, enabling them to manage complex clinical workflows that require understanding and synthesizing diverse information sources.
Additional dimensions include tool usage, reasoning capabilities, and the ability to manage multitask scenarios, which extend beyond traditional accuracy to reflect practical clinical performance.
Future opportunities involve improving evaluation methods, enhancing multimodal processing, addressing ethical and safety concerns, and fostering stronger interdisciplinary research to realize the full potential of LLMs in medicine.
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