02058nas a2200253   4500000000100000000000100001008004100002260001200043653002500055653001800080653003200098653002300130653002200153100002900175700001900204700002000223700001600243245009400259856008100353300001000434490000600444520134000450022001401790       2021                            d  c12/202110aCase-Based Reasoning10aDeep Learning10aNatural Language Processing10aEntity Recognition10aMedical Radiology1 aElvira Amador-Domínguez1 aEmilio Serrano1 aDaniel Manrique1 aJavier Bajo00aA Case-Based Reasoning Model Powered by Deep Learning for Radiology Report Recommendation  uhttps://www.ijimai.org/journal/sites/default/files/2021-11/ijimai7_2_2_0.pdf  a15-260 v73 aCase-Based Reasoning models are one of the most used reasoning paradigms in expert-knowledge-driven areas. One of the most prominent fields of use of these systems is the medical sector, where explainable models are required. However, these models are considerably reliant on user input and the introduction of relevant curated data. Deep learning approaches offer an analogous solution, where user input is not required. This paper proposes a hybrid Case-Based Reasoning, Deep Learning framework for medical-related applications, focusing on the generation of medical reports. The proposal combines the explainability and user-focused approach of case-based reasoning models with the deep learning techniques performance. Moreover, the framework is fully modular to fit a wide variety of tasks and data, such as real-time sensor captured data, images, or text, to name a few. An implementation of the proposed framework focusing on radiology report generation assistance is provided. This implementation is used to evaluate the proposal, showing that it can provide meaningful and accurate corrections, even when the amount of information available is minimal. Additional tests on the optimization degree of the case base are also performed, evidencing how the proposed framework can optimize this base to achieve optimal performance.  a1989-1660