Portrait of Hamed Ayoobi
Groningen, the Netherlands

Assistant Professor · AI for Health

Hamed Ayoobi

I develop trustworthy and interpretable AI systems that help clinicians understand, challenge, and safely use machine learning in healthcare.

At the University Medical Center Groningen, my research connects explainable AI, responsible machine learning, clinical data science, and computer vision. I work with clinicians to translate transparent AI methods into practical support for orthopaedics and medical decision-making.

View publications Google Scholar
AffiliationUMCG
FocusTrustworthy AI
ApplicationClinical decision support
MethodsXAI · LLMs · Vision

Research

Making powerful AI understandable and accountable

My work spans the methods, human questions, and clinical settings required to move responsible AI from theory into practice.

01

Explainable AI

Argumentative and prototype-driven explanations that expose the reasons, conflicts, and uncertainty behind model decisions.

02

Responsible AI

Contestable and transparent systems that support oversight, bias detection, and accountable use in high-stakes environments.

03

AI for Health

Clinically grounded machine learning for medical imaging, brain health, orthopaedics, and evidence-informed decisions.

04

Multimodal Intelligence

Interpretable deep learning across language, vision, tabular data, 3D perception, and multimodal architectures.

Now

Recent activity

Highlights

UMCG profile
  1. Argumentative Debates for Transparent Bias Detection

    Published in the Proceedings of the AAAI Conference on Artificial Intelligence.

  2. AI in Health: Beyond Black Box

    Academic presentation on transparent and responsible clinical AI.

  3. ProtoArgNet at AAAI

    Interpretable image classification using super-prototypes and computational argumentation.

Publications

Research output

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Mentorship

Student supervision

Doctoral, master’s, and bachelor’s supervision across explainable AI, deep learning, healthcare, and robotic vision.

Doctoral

Adam Dejl

Co-supervision on explainability for large language models and multimodal deep learning, Imperial College London.

Master’s

Klaske Laauwen

“Revisiting Distal Radius Fractures Instability Prediction: A Comparative Study of Modern Computer Vision Architectures and a Landmark-Based Baseline.”

Bachelor’s

Riana Sarbu

“Group-wise Argumentative Explanations for Healthcare Neural Networks.”

Background

Experience & education

Appointments

  1. Assistant Professor in AI for Health

    University Medical Center Groningen

    Trustworthy and interpretable AI for clinical decision support, with applications in orthopaedics and brain health.

  2. Postdoctoral Research Associate

    Imperial College London

    Explainable and responsible AI for neural networks, LLMs, multimodal models, medical imaging, and robotic vision.

  3. Research Intern

    ABN AMRO · Amsterdam

    Retrieval-augmented and agent-based language systems for customer service.

  4. Research Intern

    Linnaeus University · Sweden

    Online incremental machine learning and deep neural networks.

Education

  1. PhD in Artificial Intelligence

    University of Groningen

    Argumentation-based learning, open-ended 3D object recognition, and explainable robotic perception.

  2. MSc in Artificial Intelligence & Robotics

    Yazd University

    Graduated with honours (cum laude), GPA 18.56/20.

Academic community

Teaching & service

I contribute to education, peer review, and research communities across AI, robotics, and knowledge representation.

Teaching

  • Guest lecturerKnowledge and Agent Systems; Cognitive Robotics
  • LecturerDeep Neural Networks
  • Teaching assistantArguing Agents, Cognitive Robotics, Computer Vision
  • Invited speaker30 years of AI at the University of Groningen

Professional service

  • Programme committeesAAAI, ICRA, IJCAI, KR, ECAI, AISTATS
  • ReviewerScientific Reports, Data Mining and Knowledge Discovery, Supercomputing Journal
  • Workshop reviewerXAI, XLoKR, XAI-FIN, IROS
  • OrganizerXAI Seminars at Imperial College London

Recognition

Awards, funding & development

Award · 2026

Best Reviewer Award

Artificial Intelligence in Medicine (AIME) 2026 conference.

Senior Researcher

Argumentation-based Deep Interactive Explanations

Horizon 2020 European Research Council programme.

Senior Researcher

Argumentation for Interactive Explainable AI

Supported by J.P. Morgan and the Royal Academy of Engineering.

Continuing development

Advanced AI & data systems

Certificates in explainable AI, generative AI, RAG, deep learning, Databricks, SQL, and scalable data science.

Collaborate

Interested in transparent AI for health?

h.ayoobi@umcg.nl