What is it about?
MiniMedGPT is an efficient AI model for medical visual question answering, training in just 30 minutes. Using tools like Gemini Vision Pro and MediCap, it improves performance with fewer resources, aiding clinician training and supporting radiologists in accurate decision-making.
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Photo by National Cancer Institute on Unsplash
Why is it important?
What sets this work apart is its focus on efficiency and practicality in a field where most AI models require extensive resources to train. MiniMedGPT achieves competitive performance in medical visual question answering (VQA) with a lightweight design that trains in just 30 minutes—remarkably faster than traditional large models. This timeliness addresses the growing demand for AI tools that can be quickly adapted to medical applications without requiring vast computational infrastructure. By tackling challenges like dataset imbalances and language generation issues using innovative tools like Gemini Vision Pro and MediCap, this approach ensures accuracy and reliability in diverse medical scenarios. Its potential to assist in training junior clinicians and support radiologists in decision-making highlights its real-world impact. This model not only advances medical AI but also makes it more accessible and scalable, paving the way for broader adoption in healthcare systems.
Perspectives
This work introduces MiniMedGPT, a novel AI model tailored for medical visual question answering (VQA). It represents a significant step forward in making advanced AI accessible and practical for healthcare applications. By reducing training time to just 30 minutes and requiring minimal computational resources, MiniMedGPT overcomes the barriers typically associated with large vision–language models, such as high costs and complex infrastructure needs. Its importance lies in addressing critical challenges in medical AI, such as imbalanced datasets and the complexity of generating accurate language-based responses. Using innovative tools like Gemini Vision Pro and MediCap, this model ensures higher accuracy and reliability across diverse medical imaging scenarios. The implications are profound: MiniMedGPT can help train junior clinicians by simulating diagnostic challenges and serve as a decision-support tool for experienced radiologists, enhancing their efficiency and confidence. At its core, this work is about bridging the gap between cutting-edge AI and real-world healthcare, ensuring that advanced technology translates into better outcomes for patients and clinicians alike.
Dr Omar S Al-Kadi
University of Jordan
Read the Original
This page is a summary of: MiniMedGPT: Efficient Large Vision–Language Model for medical Visual Question Answering, Pattern Recognition Letters, March 2025, Elsevier,
DOI: 10.1016/j.patrec.2025.01.001.
You can read the full text:
Resources
MiniMedGPT: Efficient Large Vision-Language Model for Medical Visual Question Answering
Computational Imaging lab (http://omar.alkadi.net/1154-2/)
MiniMedGPT: Efficient Large Vision-Language Model for Medical Visual Question Answering
Highlights • Developed MiniMedGPT for efficient medical VQA, training in 30 minutes. • Addressed dataset imbalances with Gemini Vision Pro and MediCap tools. • Improved performance with minimal parameters compared to six VQA models. • Potential tool for training clinicians and supporting radiologists.
Contributors
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