<p>The growing interest in the application of Large Language Models (LLMs) for healthcare comes with a demand for better open-source LLMs, and stronger reassurances regarding their performance. To advance in this direction, this work conducts a thorough and transparent study of LLM model training and benchmarking in healthcare, releasing as open assets all resources needed for reproducing the Aloe models and its results (weights, data and code). This includes details on optimized data preprocessing and training, combining curated public data with synthetic samples for a total of 1.8B training tokens; enhanced safety, induced through Direct Preference Optimization (DPO), aligning Aloe models for ethical robustness and against jailbreaking attacks; and finally model performance, evaluated thoroughly through close-ended, open-ended, safety, and human assessments. To boost inference efficacy and test the upper bounds of open LLM performance, Aloe models are integrated with a Retrieval-Augmented Generation (RAG) system. The resultant models deliver competitive performance across healthcare benchmarks and medical fields while significantly improving safety and bias resilience. Model weights are released for research-only purposes, together with training and evaluation datasets, and RAG inference code. To enable the responsible release of such technology, this work is supported by a detailed healthcare-specific risk assessment. Building on top of base models like Llama 3.1 and Qwen 2.5, the Aloe models and their development recipe set a high standard for open-source medical LLMs, balancing top-tier performance with high ethical requirements.</p>