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Explainable AI in Healthcare
Interpretable machine learning and deep learning methods that make clinical predictions transparent, inspectable, and actionable.
Paolo Sorino, PhDPostdoctoralResearcher
Designing explainable, human-centred AI for healthcare.
Politecnico di Bari · SisInfLab
AI in HealthcareMachine LearningExplainable AIHuman–Machine InteractionClinical Decision Support
Research focus
Three connected perspectives.
From model development to explanation and interaction, each line of research is grounded in responsible, clinically meaningful use.
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AI for Clinical Decision Support
Human-centred predictive systems for risk assessment, patient stratification, and evidence-informed clinical workflows.
03
Human–Machine Interaction
Interfaces and interaction models that help people understand, question, and confidently use intelligent systems.
Secondary research lines
Graph learning & knowledge graphs · Multimodal and privacy-aware AI · Brain–computer interfaces & biosignals
47Scientific publications
25+Supervised theses
6+Research projects
01Granted national patent
About
Research at the intersection of intelligence, health, and people.
I am a postdoctoral researcher at the Department of Electrical and Information Engineering of Politecnico di Bari, and a member of SisInfLab.
My work connects artificial intelligence with real healthcare needs: building predictive methods, making their reasoning understandable, and designing interaction models that support informed human decisions.
View institutional profileResearch projects
From methods to deployed research.
I contribute to interdisciplinary projects where AI must remain scientifically rigorous, understandable, and useful to the people who rely on it.
Additional collaborations include LIFE, MISTRAL, and DEMETRA.
ISCRA-C · CINECA HPC
Principal Investigator
Competitive allocation of national high-performance computing resources for large-scale research in artificial intelligence and data science.
CALLIOPE · One Health
Work-Package Leader
Technical and scientific coordination of AI, federated learning, XAI, intelligent robotics, sensing, and the integrated One Health platform.
OMIBREED
Work-Package Leader
AI and explainability pipelines connecting genotype, phenotype, and multi-omics data for biomarker discovery and varietal classification.
REACH-XY
Work-Package Leader
AI/XAI methods and interactive systems for early detection, biological treatments, and collaborative research in plant health.
Selected publications
Research, made readable.
2026
Journal article
AMIUgraph: analysis and modeling of interactions for utility-driven benchmarking of graph-based models in healthcare
BMC Medical Informatics and Decision Making
Graph AI · Healthcare benchmarking↗
2025Journal article
Detecting label noise in longitudinal Alzheimer’s data with explainable artificial intelligence
Brain Informatics, 12(1), 15
Explainable AI · Alzheimer’s disease↗
2025Journal article
MORIX: Machine learning-aided framework for lethality detection and MORtality inference with eXplainable artificial intelligence in MAFLD subjects
Computer Methods and Programs in Biomedicine Update, 100176
Clinical prediction · XAI↗
2024Conference paper
ARIEL: Brain-Computer Interfaces meet Large Language Models for Emotional Support Conversation
ACM UMAP 2024 Adjunct, 601–609
Human-centred AI · HMI↗
Teaching & supervision
Making complex ideas work in practice.
Teaching connects my research to the next generation of engineers: from machine learning foundations to software design and applied artificial intelligence.
Professor
Fundamentals of Information Security and Data Privacy
PhD Degree CourseAdjunct Professor
Fundamentals of Generative Artificial Intelligence
Bachelor’s degree in Design · Politecnico di BariAdjunct Professor
Software Design Laboratory
Bachelor’s degree in Medical Systems Engineering · Politecnico di BariAdjunct Professor · Module E
2nd-Level Master’s Degree in Artificial Intelligence and Data Science
Politecnico di Bari · 200 hours of lectures, laboratory sessions, and tutoringAssistant Lecturer
Foundations of Machine Learning
Master’s degree in Computer Engineering · Politecnico di Bari200+Hours of specialist training
25+Bachelor’s and Master’s theses supervised
Academic service
Contributing beyond publications.
Associate Editor
IEEE Journal of Biomedical and Health Informatics (J-BHI) · Public Health Informatics Section · Q1 journal.
Guest Editor
Sensors Special Issue on Advances in Sensorized AI-Driven Intelligent Systems in Healthcare and Beyond.
International engagement
Invited lectures, workshop organization, programme committees, and session leadership across AI, HMI, and healthcare research.
Granted patent
Machine-learning method for validating NAFLD diagnosis without imaging technologies.
Granted patent
Method for neural control of self-propelled devices
Invited talks & lectures
International invitations spanning explainable healthcare AI, human–machine systems, and brain–computer interaction.
ISACT 2026 · IEEE ICHMS 2026
AI for BCI with Applications
Nanyang Technological University, Singapore · 1–3 July 2026
WOA 2026 · Mini-School Lecture
Artificial Intelligence and Explainable AI in Clinical Decision Support Systems: Innovation, Interpretability, and Trust
27th Workshop “From Objects to Agents” · Salerno · 17 June 2026
IEEE SMC · 15th BMI Systems Workshop
Brain Computer Interfaces for Neural Games
Special event: Integrative Approaches to EEG Signal Analysis
ISACT 2024 · IEEE SMC Society
Artificial Intelligence for Brain-Computer Interaction
Hosted by University of Naples Federico II · 10–13 December 2024
IEEE BHI-BSN 2022 · Invited Speech
Real-Time Music Composition Based on AI-Driven Emotion Recognition
Workshop on enabling technologies for paediatric rehabilitation · Ioannina, Greece · 27–30 September 2022
Let’s connect
Interested in explainable,
human-centred healthcare AI?
I am open to research collaborations, invited talks, and projects that bring transparent AI into meaningful real-world settings.
paolo.sorino@poliba.it Download full CV