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Desarrollo de un módulo de formación integrado y contextual basado en grandes modelos lingüísticos para la educación
Anna Romanyukha1, Mahta Mazloumi1, Thomas De Waelheyns1
1Qaelum NV, Leuven, Belgium.
Resumen
Un nuevo modelo de IA ofrece a los profesionales de la salud un acceso rápido y fiable a la información sobre protección radiológica. Este modelo de lenguaje amplio específico del dominio (LLM) utiliza el conocimiento controlado para la educación continua, mejorando la investigación y la formación.
Área de la Ciencia:
- Inteligencia artificial en el cuidado de la salud
- Ciencias de la protección radiológica
- Tecnología de la educación médica
Sus antecedentes:
- Los grandes modelos de lenguaje (LLM) requieren ajuste específico de dominio para una entrega de información confiable.
- La educación sanitaria y la investigación requieren conocimientos científicos precisos y de fácil acceso.
- La educación continua en protección radiológica es crucial para los profesionales e investigadores.
Objetivo del estudio:
- Desarrollar un LLM específico para la educación continua en protección radiológica.
- Permitir a los usuarios consultar temas específicos y recibir información científica fiable.
- Reducir la necesidad de revisión manual de materiales educativos extensos.
Principales métodos:
- Desarrolló y entrenó un LLM específico del dominio utilizando conocimientos personalizados de protección contra la radiación.
- Se ha probado el modelo con varios escenarios y hiperparámetros ajustados (por ejemplo, k superior, tamaño del trozo, temperatura).
- Evaluación del rendimiento del modelo basado en la precisión y fiabilidad de las respuestas.
Principales resultados:
- El LLM proporcionó respuestas fiables y precisas a diversas consultas de los usuarios sobre temas de protección radiológica.
- El ajuste de hiperparámetros, particularmente el modelo de incrustación y el corte de similitud, afectaron significativamente el rendimiento.
- El modelo procesó y entregó con éxito información de materiales educativos controlados.
Conclusiones:
- El LLM validado apoya efectivamente la educación continua en protección radiológica.
- Los usuarios pueden acceder de forma rápida y fiable a la información sobre radiobiología y seguridad radiológica.
- Esta herramienta de IA mejora el aprendizaje para los profesionales de la salud y los investigadores en el campo.
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