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Diagnostics
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Deep Learning in Medical Imaging: Challenges and Opportunities
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Deep Learning in Medical Imaging: Challenges and Opportunities

A Special Issue of Diagnostics (ISSN 2075-4418) belonging to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (31 July 2026) | Viewed by 3513

Editor

Dr. Morten Bo Søndergaard Svendsen

E-Mail Website
Guest Editor
Department of Health Technology, Technical University of Denmark, Ørsteds Plads, Building 345C, Room 109, 2800 Kongens Lyngby, Denmark
Interests: artificial intelligence; medical imaging; image segmentation; medical image processing

Special Issue Information

Dear Colleagues,

Deep learning has revolutionized the field of medical imaging, offering unprecedented opportunities to enhance diagnostic accuracy, streamline workflows, and personalize patient care. This Special Issue explores the transformative potential of deep learning in medical imaging, highlighting its applications in areas such as image segmentation, disease detection, and predictive analytics. It also addresses the challenges associated with implementing these technologies, including data scarcity, model interpretability, and integration into clinical practice. Contributions from leading researchers and practitioners showcase innovative approaches to overcoming these barriers, such as transfer learning, federated learning, and explainable AI. By bridging the gap between cutting-edge research and real-world clinical needs, this Special Issue aims to provide a comprehensive overview of the current state and future directions of deep learning in medical imaging, offering valuable insights for researchers, clinicians, and healthcare professionals alike.

Dr. Morten Bo Søndergaard Svendsen
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • deep learning
  • artificial intelligence
  • medical imaging
  • endoscopy
  • image segmentation
  • medical image processing

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Published Papers (1 paper)

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Research

16 pages, 1699 KB  
Article
A Comparative Assessment of ChatGPT, Gemini, and DeepSeek Accuracy: Examining Visual Medical Assessment in Internal Medicine Cases with and Without Clinical Context
by Rayah Asiri, Azfar Athar Ishaqui, Salman Ashfaq Ahmad, Muhammad Imran, Khalid Orayj and Adnan Iqbal
Diagnostics 2026, 16(3), 388; https://doi.org/10.3390/diagnostics16030388 - 26 Jan 2026
Cited by 4 | Viewed by 2201
Abstract
Background and Aim: Large language models (LLMs) demonstrate significant potential in assisting with medical image interpretation. However, the diagnostic accuracy of general-purpose LLMs on image-based internal medicine cases and the added value of brief clinical history remain unclear. This study evaluated three general-purpose [...] Read more.
Background and Aim: Large language models (LLMs) demonstrate significant potential in assisting with medical image interpretation. However, the diagnostic accuracy of general-purpose LLMs on image-based internal medicine cases and the added value of brief clinical history remain unclear. This study evaluated three general-purpose LLMs (ChatGPT, Gemini, and DeepSeek) on expert-curated cases to quantify diagnostic accuracy with image-only input versus image plus brief clinical context. Methods: We conducted a comparative evaluation using 138 expert-curated cases from Harrison’s Visual Case Challenge. Each case was presented to the models in two distinct phases: Phase 1 (image only) and Phase 2 (image plus a brief clinical history). The primary endpoint was top-1 diagnostic accuracy for the textbook diagnosis, comparing performance with versus without a brief clinical history. Secondary/Exploratory analyses compared models and assessed agreement between model-generated differential lists and the textbook differential. Statistical analysis included Wilson 95% confidence intervals, McNemar’s tests, Cochran’s Q with Benjamini–Hochberg correction, and Wilcoxon signed-rank tests. Results: The inclusion of clinical history substantially improved diagnostic accuracy for all models. ChatGPT’s accuracy increased from 50.7% in Phase 1 to 80.4% in Phase 2. Gemini’s accuracy improved from 39.9% to 72.5%, and DeepSeek’s accuracy rose from 30.4% to 75.4%. In Phase 2, diagnostic accuracy reached at least 65% across most disease nature and organ system categories. However, agreement with the reference differential diagnoses remained modest, with average overlap rates of 6.99% for ChatGPT, 36.39% for Gemini, and 32.74% for DeepSeek. Conclusions: The provision of brief clinical history significantly enhances the diagnostic accuracy of large language models on visual internal medicine cases. In this benchmark, performance differences between models were smaller in Phase 2 than in Phase 1. While diagnostic precision improves markedly, the models’ ability to generate comprehensive differential diagnoses that align with expert consensus is still limited. These findings underscore the utility of context-aware, multimodal LLMs for educational support and structured diagnostic practice in supervised settings while also highlighting the need for more sophisticated, semantics-sensitive benchmarks for evaluating diagnostic reasoning. Full article
(This article belongs to the Special Issue Deep Learning in Medical Imaging: Challenges and Opportunities)
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