Creation and Adoption of Large Language Models in Medicine
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View abstract on PubMed
Summary
This summary is machine-generated.Large language models (LLMs) offer medical benefits, but active involvement is crucial. We must guide LLM development with medical data and real-world testing to ensure safe and effective integration into healthcare.
Area Of Science
- Medical Informatics
- Artificial Intelligence in Healthcare
- Clinical Decision Support
Background
- Growing interest in large language models (LLMs) for potential medical applications.
- Current LLM applications in medicine often lack specific medical training data and benefit verification.
- Risk of losing agency in shaping LLM integration if not actively involved.
Purpose Of The Study
- To emphasize the need for active engagement in shaping the use of LLMs in medicine.
- To highlight the importance of responsible development and deployment of LLM-powered medical tools.
- To advocate for a proactive approach in integrating artificial intelligence into healthcare.
Main Methods
- Analysis of current trends in LLM adoption in medical tasks.
- Identification of gaps in LLM training and validation for healthcare.
- Framework proposal for active shaping of LLM development and deployment.
Main Results
- LLM applications are increasingly used for medical tasks without adequate medical data training or benefit validation.
- Passive observation risks ceding control over how LLMs reshape medical practice.
- Active shaping requires provisioning relevant data, defining benefits, and real-world evaluation.
Conclusions
- Active involvement is essential to harness the benefits of LLMs in medicine responsibly.
- Provisioning medical data, specifying benefits, and real-world testing are critical for safe LLM integration.
- Proactive shaping ensures LLMs serve to enhance, rather than disrupt, medical practice.
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