Jose M. Juarez

Medical AI

I research on cutting edge AI technologies, such as machine learning, reinforcement and deep learning, for solving clinical problems in real life scenarios. This research aims to help doctors and patients to have a better healthcare service.

eXplainable AI

My research on eXplainable AI (XAI) helps me understand how AI systems make decisions, so I can trust and use them more confidently. When I know why/how an AI gave an answer, I can spot errors or biases and feel more secure relying on its help.

About Me

Jose M. Juarez is Full Professor (catedrático) at the University of Murcia, specializing in Artificial Intelligence. His research work spans machine learning in healthcare, data simulation/visualization and eXplainable artificial intelligence. He teaches future generation engineers (data sc. and computer sc.) and healthcare professionals on the principles and use of Artificial Intelligence.

In summary: AI Research + Health + People

Awards and recognitions

Best Paper Award at AIME (2025), coordinator of AI master studies at Univ. Murcia (2025), associate editor of the Journal of AI in Medicine (since 2025), best paper student award HealthInf (2024), co-founder of the Spanish Society of AI in Biomedicine (2023), Board Member of the AI in Medicine Society (2022), co-founder of the XAI-Healthcare workshop (2021).

Introducción a la IA

Introduction of fundamentals of AI for 1st year Data Engeneering students. In this course you will find computational logics, graph search principles, rule-based systems and essentials to Machine Learning.

Inteligencia Artificial I

Seminars for 3rd year students on Computer Science. Main AI methods covering heuristic search, games and planning.

Machine Learning 2

Advance models for 3rd year students on Data Engineers. Deep dives into data preparation for ML models, bayesian networks, Makov models and genetic algorithms.

Explainable ML

High advanced AI content for Master in AI students. Methods for interpreting and explaining machine learning and deep learning models.

Use of genAI in clinical practice

Industry focuded seminars for medical doctors and healthcare professionals to introduce generative AI.

AI in One Health

Course focused on the use of AI for human and animal health professionals from the One Health and Global Health master's program.

AI Thesis Supervision

Some of the students I was honored to advise on Artificial Intelligence.

• Denisse M. Kim, PhD 2024: PhD Thesis: 'Simulation and Visualization of Spatial-Temoral Data for Hospital Outbreak Infections'.
• Antonio Lopez Mtnez-Cararasco, PhD 2024: PhD Thesis: 'Clustering and Subgroup Discovery for Patient Phenotyping'.
• Isidoro Casanova, PhD 2023: IT division of the Regional Government. PhD Thesis: 'Using multivariate sequential patterns for classification and temporal knowledge extraction. A survival study of patients in a critical care burn unit'.
• Eduardo Lupiani, PhD 2014: currently at NTT Data, Germany. PhD Thesis: 'Temporal case-base maintentance'.

Medical Informatics PhD Supervision

Some of the students I was honored to advise on the Medical Informatics field.

• Lorena Pujante, PhD 2025: 'Combining spatial-temporal modeling and reasoning with graph technologies applied to the epidemiology of nosocomial infections and multidrug-resistant infections'.
• Natalia Iglesias, PhD 2021: CTO Savana. PhD Thesis: 'Methodology and effective representation of knowledge of clinical guidelines and clinical protocols'.
• Bernardo Canovas-Segura, PhD 2019: associate professor. PhD Thesis: 'Medical informatics approaches for decision support in antimicrobial stewardship'.