Asset Details
Do you wish to reserve the book?
Navigating the integration of large language models in healthcare: challenges, opportunities, and implications under the EU AI Act
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Navigating the integration of large language models in healthcare: challenges, opportunities, and implications under the EU AI Act
Do you wish to request the book?
Navigating the integration of large language models in healthcare: challenges, opportunities, and implications under the EU AI Act
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Navigating the integration of large language models in healthcare: challenges, opportunities, and implications under the EU AI Act
2024
Overview
Discussion Transformative potential of LLMs in healthcare LLMs are increasingly recognized for their ability to process vast datasets and generate human-like text, with applications spanning medical diagnostics, administrative tasks, and patient engagement [3]. LLMs process large datasets that may inadvertently include sensitive patient information, raising questions about data security and consent. Summary table: integration of large language models in healthcare Category Key points Transformative potential Streamlines clinical workflows with tasks like medical document summarization and discharge summaries Enhances medical education and personalized care through rapid synthesis of literature and tailored health advice Supports multimodal LLMs (M-LLMs) for integrating text, images, and sensor data to improve diagnostic accuracy Challenges Concerns over data privacy, particularly handling sensitive patient information Risks of biased outputs from nonrepresentative datasets perpetuating healthcare inequities Issues with model reliability and interpretability in critical clinical decisions Ethical concerns Ensuring fairness and transparency in AI-generated content Addressing biases to prevent exacerbating existing disparities in healthcare delivery Safeguarding patient trust through robust ethical and regulatory oversight Role of EU AI Act Establishes a risk-based framework categorizing AI systems by risk (limited, high, and unacceptable) Mandates transparency and prohibits high-risk systems like biometric categorization Encourages innovation via regulatory sandboxes, balancing progress with safety Future directions Calls for interdisciplinary collaboration among clinicians, technologists, and ethicists Promotes continuous evaluation and refinement of AI models to align with evolving healthcare needs Advocates for global standardization of regulations for consistent AI governance Conclusion LLMs present a dual-edged sword: immense potential to enhance healthcare delivery paired with challenges that demand meticulous oversight. Cascella, M; Montomoli, J; Bellini, V; Bignami, E. Evaluating the feasibility of ChatGPT in healthcare: an analysis of multiple clinical and research scenarios.
Publisher
BioMed Central,Springer Nature B.V,BMC
