Bridging healthcare gaps: a scoping review on the role of artificial intelligence, deep learning, and large language models in alleviating problems in medical deserts
View abstract on PubMed
Summary
This summary is machine-generated.Artificial intelligence (AI) and large language models (LLMs) can improve healthcare in underserved "medical deserts." These technologies enhance access, quality, and resource allocation, bridging critical healthcare gaps.
Area Of Science
- Health Informatics
- Medical Artificial Intelligence
- Digital Health
Background
- Medical deserts
Purpose Of The Study
- Examine how AI, particularly LLMs, can address healthcare challenges in underserved areas.
- Investigate AI integration with e-Health and the Internet of Medical Things for enhanced service delivery.
Main Methods
- Qualitative narrative review of AI applications in healthcare.
- Analysis of AI-driven telehealth, diagnostic assistance, chatbots, medical education, and resource allocation.
Main Results
- AI-powered telehealth platforms overcome barriers, improving accessibility.
- LLMs offer diagnostic support and preliminary medical advice, supplementing expertise in remote areas.
- AI enhances medical training and optimizes resource allocation for better healthcare distribution.
Conclusions
- AI, especially LLMs, presents a promising solution for bridging healthcare gaps in medical deserts.
- Strategic implementation of AI can significantly improve service accessibility, quality, and distribution.
- Further research is crucial to fully harness AI's potential in addressing medical desert challenges.
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