Reza Basiri

Research

Book chapter, September 2025 Open access

Applications of Generative AI in Diabetic Foot Ulcer Treatment

Reza Basiri

IntechOpen. DOI 10.5772/intechopen.1012472

In brief

A single-author review of how generative AI is changing diabetic foot ulcer care: synthetic images where real data is scarce, automated clinical documentation, treatment planning and healing forecasts. It also covers what clinical validation and the new regulatory guidance for AI-enabled devices mean for getting these tools into practice.

Key findings

  • Four application areas: synthetic imaging, documentation, treatment planning and prediction
  • Privacy-preserving, multi-site training through federated learning
  • A regulatory view of the path to deployment, including the FDA's 2025 guidance on AI-enabled devices

Read at the publisher

Related work

  1. Multimodal Artificial Intelligence System to Enhance the Classification and Analysis of Diabetic Foot Ulcer Healing Conditions

    Reza Basiri

    PhD thesis. University of Toronto, 2025 Open access

    The doctoral work that ties the research programme together. It builds the Zivot dataset, ranks the clinical signs that matter most for healing, measures how colour, thermal and depth imaging complement each other, and introduces an attention-based network that fuses all of them to classify a wound's healing phase.

    SummaryFull text

  2. Enhancing Diabetic Foot Ulcer Assessment Through Fine-Tuned Vision-Language Models

    Reza Basiri, Asad Ghaffar, Donya Ghiasi, Meaka Tesfaye Mekonnen, Milos R. Popovic, Shehroz S. Khan

    Conference paper. IJCAI 2025 workshops, Communications in Computer and Information Science (Springer), 117-131, 2025

    General-purpose vision-language models can describe a photograph, but not the way a wound-care clinician would. This work fine-tunes vision-language models on diabetic foot ulcer images so they assess and describe wounds in clinical terms, moving from the off-the-shelf approach of UlcerGPT to models adapted to the domain.

    SummaryPublisher

  3. UlcerGPT: A Multimodal Approach Leveraging Large Language and Vision Models for Diabetic Foot Ulcer Image Transcription

    Reza Basiri, Ali Abedi, Chau Nguyen, Milos R. Popovic, Shehroz S. Khan

    Conference paper. ICPR 2024 workshops (PRHA), Lecture Notes in Computer Science (Springer), 221-232, 2024

    UlcerGPT pairs a vision-language model (LLaVA) with a large language model (ChatGPT) to turn a photo of a diabetic foot ulcer into a written clinical description: what is in the image, where the regions of concern are, and how they should be classified. Expert clinicians rated its output on a public dataset.

    SummaryPublisherarXiv