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Diagnostics
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Generative AI and Digital Twins in Diagnostics
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Generative AI and Digital Twins in Diagnostics

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

Deadline for manuscript submissions: closed (30 June 2026) | Viewed by 3516

Editor

Dr. Sean Benson

E-Mail Website
Guest Editor
Department of Cardiology, Amsterdam University Medical Centers, 1105 Amsterdam, AZ, The Netherlands
Interests: generative AI; digital twins; cardiovascular disease
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The convergence of digital twins and generative AI represents a ground-breaking frontier in medical research and clinical practice. A digital twin is a dynamic, virtual model of a physical entity such as a patient, organ, or cohort. Typically, such models are created using data from electronic health records, wearable devices, or clinical imaging. This technology offers unprecedented opportunities for personalized medicine, allowing for the precise modeling of individual patient physiology, prediction of disease progression, and tailoring of treatment strategies. Generative AI, with its ability to create new data, simulations, and models, further enhances the potential of digital twins. This technology can accelerate drug discovery, improve diagnostic accuracy, and enable the development of highly personalized therapeutic approaches.

This Special Issue aims to explore the synergy between digital twins and generative AI in medicine, showcasing cutting-edge research, novel applications, and theoretical advancements. We seek contributions that address a broad range of topics, including, but not limited to, the creation and validation of digital twins, the integration of generative AI into clinical workflows, ethical and regulatory considerations, and case studies demonstrating the real-world impact of these technologies.

We invite researchers and clinicians to submit original research, reviews, perspectives, or case studies applying personalized simulations and generative models to clinical data.

Dr. Sean Benson
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

  • digital twins
  • electronic health record
  • generative AI
  • personalized medicine
  • clinical practice
  • simulation

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

13 pages, 1819 KB  
Article
Diagnostic Performance of ChatGPT-5 for Detecting Pediatric Pneumothorax on Chest Radiographs: A Multi-Prompt Evaluation
by Chih-Hao Wang, Po-Chih Lin, Shin-Lin Shih, Pei-Shan Tsai and Wen-Hui Huang
Diagnostics 2026, 16(2), 232; https://doi.org/10.3390/diagnostics16020232 - 11 Jan 2026
Cited by 2 | Viewed by 1315
Abstract
Background/Objectives: Chest radiography is the primary first-line imaging tool for diagnosing pneumothorax in pediatric emergency settings. However, interpretation under clinical pressures such as high patient volume may lead to delayed or missed diagnosis, particularly for subtle cases. This study aimed to evaluate [...] Read more.
Background/Objectives: Chest radiography is the primary first-line imaging tool for diagnosing pneumothorax in pediatric emergency settings. However, interpretation under clinical pressures such as high patient volume may lead to delayed or missed diagnosis, particularly for subtle cases. This study aimed to evaluate the diagnostic performance of ChatGPT-5, a multimodal large language model, in detecting and localizing pneumothorax on pediatric chest radiographs using multiple prompting strategies. Methods: In this retrospective study, 380 pediatric chest radiographs (190 pneumothorax cases and 190 matched controls) from a tertiary hospital were interpreted using ChatGPT-5 with three prompting strategies: instructional, role-based, and clinical-context. Performance metrics, including accuracy, sensitivity, specificity, and conditional side accuracy, were evaluated against an expert-adjudicated reference standard. Results: ChatGPT-5 achieved an overall accuracy of 0.77–0.79 and consistently high specificity (0.96–0.98) across all prompts, with stable reproducibility. However, sensitivity was limited (0.57–0.61) and substantially lower for small pneumothoraces (American College of Chest Physicians [ACCP]: 0.18–0.22; British Thoracic Society [BTS]: 0.41–0.46) than for large pneumothoraces (ACCP: 0.75–0.79; BTS: 0.85–0.88). The conditional side accuracy exceeded 0.96 when pneumothorax was correctly detected. No significant differences were observed among prompting strategies. Conclusions: ChatGPT-5 showed consistent but limited diagnostic performance for pediatric pneumothorax. Although the high specificity and reproducible detection of larger pneumothoraces reflect favorable performance characteristics, the unacceptably low sensitivity for subtle pneumothoraces precludes it from independent clinical interpretation and underscores the necessity of oversight by emergency clinicians. Full article
(This article belongs to the Special Issue Generative AI and Digital Twins in Diagnostics)
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14 pages, 250 KB  
Article
Concordance Between the Multidisciplinary Team and ChatGPT-4o Decisions: A Blinded, Cross-Sectional Concordance Study in Systemic Autoimmune Rheumatic Diseases
by Firdevs Ulutaş, Göksel Altınışık, Gülay Güngör, Vefa Çakmak, Nilüfer Yiğit, Duygu Herek, Murat Yiğit, Uğur Karasu and Veli Çobankara
Diagnostics 2026, 16(1), 113; https://doi.org/10.3390/diagnostics16010113 - 30 Dec 2025
Viewed by 1022
Abstract
Background/Objective: In recent years, artificial intelligence (AI) has gained increasing prominence in the fields of diagnostic decision-making in medicine. The aim of this study was to compare multidisciplinary team (MDT: rheumatology, pulmonology, thoracic radiology) decisions with single-session plans generated by ChatGPT-4o. Methods: In [...] Read more.
Background/Objective: In recent years, artificial intelligence (AI) has gained increasing prominence in the fields of diagnostic decision-making in medicine. The aim of this study was to compare multidisciplinary team (MDT: rheumatology, pulmonology, thoracic radiology) decisions with single-session plans generated by ChatGPT-4o. Methods: In this cross-sectional concordance study, adults (≥18 years) with confirmed systemic autoimmune rheumatic disease (SARD) and having MDT decisions within the last 6 months were included. The study documented diagnostic, treatment, and monitoring decisions in cases of SARDs by recording answers to six essential questions: (1) What is the most likely clinical diagnosis? (2) What is the most likely radiological diagnosis? (3) Is there a need for anti-inflammatory treatment? (4) Is there a need for antifibrotic treatment? (5) Is drug-free follow-up appropriate? and (6) Are additional investigations required? Consequently, all evaluations were performed with ChatGPT-4o in a single-session format using a standardized single-prompt template, with the system blinded to MDT decisions. All data analyses in this study were conducted using the R programming language (version 4.3.2). An agreement between AI-generated and MDT decisions was assessed using Cohen’s Kappa (κ) statistic where κ (kappa) values represent the level of agreement: <0.20 = slight, 0.21–0.40 = fair, 0.41–0.60 = moderate, 0.61–0.80 = substantial, >0.80 = almost perfect agreement. These analyses were performed using the irr and psych packages in R. Statistical significance of the models was evaluated through p-values, while overall model fit was assessed using the Likelihood Ratio Test. Results: A total of 47 patients were involved in this study, with a predominance of female patients (61.70%, n = 29). The mean age was 61.74 ± 10.40 years. The most frequently observed diagnosis was rheumatoid arthritis (RA), accounting for 31.91% of cases (n = 15). This was followed by cases of anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis, interstitial pneumonia with autoimmune features (IPAF), and sarcoidosis. The analyses indicate a statistically significant level of agreement across all decision types. For clinical diagnosis decisions, agreement was moderate (κ = 0.52), suggesting that the AI system can reach partially consistent conclusions in diagnostic processes. The need for an immunosuppressive treatment and follow-up without medication decisions demonstrated a higher level of concordance, reaching the moderate-to-high range (κ = 0.64 and κ = 0.67, respectively). For antifibrotic treatment decisions, agreement was moderate (κ = 0.49), while radiological diagnosis decisions also fell within the moderate range (κ = 0.55). The lowest agreement—though still moderate—was observed in further investigation required decisions (κ = 0.45). Conclusions: In patients with SARDs with pulmonary involvement, particularly in complex cases, concordance was observed between MDT decisions and AI-generated recommendations regarding prioritization of clinical and radiologic diagnoses, treatment selection, suitability for drug-free follow-up, and the need for further diagnostic investigations. Full article
(This article belongs to the Special Issue Generative AI and Digital Twins in Diagnostics)
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