The increasing availability of large language models (LLMs) such as ChatGPT has accelerated their use in clinical settings, particularly for answering medical questions, supporting diagnosis, and assisting patient education. With the evolution toward multimodal versions such as ChatGPT-4, it has become possible to combine image analysis with text interpretation, bringing these systems closer to more comprehensive clinical support. However, while specialized deep learning models such as CNNs have already demonstrated high accuracy in image analysis in gastroenterology, the performance of general-purpose LLMs in this domain remains underexplored and still requires robust validation.
Home / Publications / Publication
Home / Publications / Publication
CHATGPT-4 PERFORMANCE IN INTERPRETING MEDICAL IMAGES IN GASTROENTEROLOGY: POTENTIAL AND LIMITATIONS
Publication Type: Paper Abstract
Original title: Evaluating ChatGPT-4 for the Interpretation of Images from Several Diagnostic Techniques in Gastroenterology
Article publication date: January 2025
Source: Journal of Clinical Medicine (MDPI)
Author(s): Miguel Mascarenhas Saraiva; Tiago Ribeiro; Belén Agudo; João Afonso; Francisco Mendes; Miguel Martins; Pedro Cardoso; Joana Mota; Maria João Almeida; António Costa; Mariano Gonzalez Haba Ruiz; Jessica Widmer; Eduardo Moura; Ahsan Javed; Thiago Manzione; Sidney Nadal; Luis F. Barroso; Vincent de Parades; João Ferreira; Guilherme Macedo
What is the goal, target audience, and areas of digital health it addresses?
The study aims to systematically evaluate ChatGPT-4’s performance in the automatic interpretation of medical images from multiple diagnostic techniques in gastroenterology. The target audience includes gastroenterologists, clinicians, researchers in artificial intelligence applied to healthcare, and decision-makers involved in digital health transformation. The publication is mainly positioned in the areas of generative and multimodal artificial intelligence, medical image analysis, clinical decision support systems, and integration of clinical and imaging data.
What is the context?
The increasing availability of large language models (LLMs) such as ChatGPT has accelerated their use in clinical settings, particularly for answering medical questions, supporting diagnosis, and assisting patient education. With the evolution toward multimodal versions such as ChatGPT-4, it has become possible to combine image analysis with text interpretation, bringing these systems closer to more comprehensive clinical support. However, while specialized deep learning models such as CNNs have already demonstrated high accuracy in image analysis in gastroenterology, the performance of general-purpose LLMs in this domain remains underexplored and still requires robust validation.
What are the current approaches?
The current dominant approaches in medical image analysis rely on convolutional neural networks (CNNs) and other specialized deep learning models trained on large annotated clinical datasets. These models have achieved strong results in lesion detection, endoscopy, pancreatic diagnosis, and cancer detection. In contrast, multimodal LLMs such as ChatGPT-4 combine text and visual interpretation but were not originally designed as dedicated vision models. Their performance may therefore depend on integration with external vision components, which can limit effectiveness in highly specialized clinical tasks.
What is the innovation and how was its impact evaluated?
The main innovation of the study lies in evaluating a general-purpose model, ChatGPT-4, in tasks traditionally dominated by specialized models. The system was tested on 740 clinical images from five techniques: capsule endoscopy, device-assisted enteroscopy, endoscopic ultrasound (EUS), digital single-operator cholangioscopy (DSOC), and high-resolution anoscopy (HRA). Each image was analyzed using structured prompts, and the outputs were compared with the reference diagnosis used as the gold standard. The study evaluated impact through standard clinical performance metrics, including accuracy, sensitivity, specificity, positive predictive value and negative predictive value (PPV/NPV), as well as AUC.
What are the main results, conclusions, and future implications?
The results show variable performance across techniques. In capsule endoscopy, accuracy ranged from 50% to 90%, with relevant differences across anatomical locations. In device-assisted enteroscopy, overall accuracy was 67%. In endoscopic ultrasound, accuracy ranged from approximately 40% to 55%, revealing limited ability to differentiate pancreatic lesions. In digital single-operator cholangioscopy, accuracy was about 55% for malignancy detection. In high-resolution anoscopy, accuracy ranged from 47.5% to 67.5%, depending on the stage of the examination.
Overall, performance was suboptimal and inconsistent, remaining below that of specialized models such as CNNs, which can achieve accuracies above 90% to 99% in similar tasks. The study highlights a significant risk of diagnostic error, including false positives and false negatives. In the short term, direct clinical use is not recommended, and its utility as a standalone tool remains limited. In the medium to long term, there is potential for a hybrid support model combining text and images, but substantial improvements in the visual component will be required. The main identified risks include incorrect diagnoses with negative clinical impact and a possible loss of trust among professionals and patients.
Would you like to know all the details?
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Home / Publications / Publication
CHATGPT-4 PERFORMANCE IN INTERPRETING MEDICAL IMAGES IN GASTROENTEROLOGY: POTENTIAL AND LIMITATIONS
Publication Type: Paper Abstract
Original title: Evaluating ChatGPT-4 for the Interpretation of Images from Several Diagnostic Techniques in Gastroenterology
Article publication date: January 2025
Source: Journal of Clinical Medicine (MDPI)
Author(s): Miguel Mascarenhas Saraiva; Tiago Ribeiro; Belén Agudo; João Afonso; Francisco Mendes; Miguel Martins; Pedro Cardoso; Joana Mota; Maria João Almeida; António Costa; Mariano Gonzalez Haba Ruiz; Jessica Widmer; Eduardo Moura; Ahsan Javed; Thiago Manzione; Sidney Nadal; Luis F. Barroso; Vincent de Parades; João Ferreira; Guilherme Macedo
What is the goal, target audience, and areas of digital health it addresses?
The study aims to systematically evaluate ChatGPT-4’s performance in the automatic interpretation of medical images from multiple diagnostic techniques in gastroenterology. The target audience includes gastroenterologists, clinicians, researchers in artificial intelligence applied to healthcare, and decision-makers involved in digital health transformation. The publication is mainly positioned in the areas of generative and multimodal artificial intelligence, medical image analysis, clinical decision support systems, and integration of clinical and imaging data.
What is the context?
The increasing availability of large language models (LLMs) such as ChatGPT has accelerated their use in clinical settings, particularly for answering medical questions, supporting diagnosis, and assisting patient education. With the evolution toward multimodal versions such as ChatGPT-4, it has become possible to combine image analysis with text interpretation, bringing these systems closer to more comprehensive clinical support. However, while specialized deep learning models such as CNNs have already demonstrated high accuracy in image analysis in gastroenterology, the performance of general-purpose LLMs in this domain remains underexplored and still requires robust validation.
What are the current approaches?
The current dominant approaches in medical image analysis rely on convolutional neural networks (CNNs) and other specialized deep learning models trained on large annotated clinical datasets. These models have achieved strong results in lesion detection, endoscopy, pancreatic diagnosis, and cancer detection. In contrast, multimodal LLMs such as ChatGPT-4 combine text and visual interpretation but were not originally designed as dedicated vision models. Their performance may therefore depend on integration with external vision components, which can limit effectiveness in highly specialized clinical tasks.
What is the innovation and how was its impact evaluated?
The main innovation of the study lies in evaluating a general-purpose model, ChatGPT-4, in tasks traditionally dominated by specialized models. The system was tested on 740 clinical images from five techniques: capsule endoscopy, device-assisted enteroscopy, endoscopic ultrasound (EUS), digital single-operator cholangioscopy (DSOC), and high-resolution anoscopy (HRA). Each image was analyzed using structured prompts, and the outputs were compared with the reference diagnosis used as the gold standard. The study evaluated impact through standard clinical performance metrics, including accuracy, sensitivity, specificity, positive predictive value and negative predictive value (PPV/NPV), as well as AUC.
What are the main results, conclusions, and future implications?
The results show variable performance across techniques. In capsule endoscopy, accuracy ranged from 50% to 90%, with relevant differences across anatomical locations. In device-assisted enteroscopy, overall accuracy was 67%. In endoscopic ultrasound, accuracy ranged from approximately 40% to 55%, revealing limited ability to differentiate pancreatic lesions. In digital single-operator cholangioscopy, accuracy was about 55% for malignancy detection. In high-resolution anoscopy, accuracy ranged from 47.5% to 67.5%, depending on the stage of the examination.
Overall, performance was suboptimal and inconsistent, remaining below that of specialized models such as CNNs, which can achieve accuracies above 90% to 99% in similar tasks. The study highlights a significant risk of diagnostic error, including false positives and false negatives. In the short term, direct clinical use is not recommended, and its utility as a standalone tool remains limited. In the medium to long term, there is potential for a hybrid support model combining text and images, but substantial improvements in the visual component will be required. The main identified risks include incorrect diagnoses with negative clinical impact and a possible loss of trust among professionals and patients.
Would you like to know all the details?
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With more than half a million hospital medical consultations carried out in the first half of 2024, the ULS of Coimbra stands out as an institution dedicated to integrated, high-quality and patient-centered healthcare, with 8…
Elderly Care Ecosystem: an Innovative Platform for Personalized and Efficient Services
The Elderly Care Ecosystem (ECE) is an integration of various digital health technologies, exploring the areas of telehealth and predictive analytics. The goal of this ecosystem is to improve the quality of life for elderly…
Innovative technology that subconsciously relieves anxiety through a scarf
The SCAARF technology aims to offer an alternative method to alleviate anxiety symptoms in a non-intrusive and subconscious way. This technology is an innovative idea in the field of digital health and wearable technology, designed…
Digital Health Interventions: Equity in Hypertension Care for Everyone
Nearly half of all adults in the United States have hypertension, one of the leading risk factors for cardiovascular disease, and only about a quarter (24%) of those people have their hypertension under control. Studies…
Personalization and Technology in Diabetes Management
IPDM has significant potential to improve diabetes management and drive health system reforms to become high-performing, effective, equitable, accessible, and sustainable. Evidence and good practices inspire health system transformation. Adopting person-centred approaches like co-creation and…
Negotiations on the European Health Data Space Advance With the Participation of the SPMS
The European Health Data Space will be a common health data sharing system across the European Union. It foresees the use of data for purposes that benefit people and society. It will ensure citizens have…
Secretary of State Margarida Tavares Emphasizes Digital Innovation in Health Promotion
Margarida Tavares spoke at the opening of the conference ” O Digital na promoção contínua da saúde e do bem-estar”, organized by the Associação para a Promoção e Desenvolvimento da Sociedade da Informação (APDSI) and…
ARS Algarve Modernizes Radiology With AI and New Data Center
The radiology service of ARS Algarve has already performed nearly 29,000 exams using Artificial Intelligence (AI) technology. In recent years, there has been a significant investment in image digitization and data storage, as well as…
European Health Data Space: Unified Access To Health Data In The EU
The COVID-19 pandemic highlighted the importance of digital services in health, but complex rules and increasing cyberattacks make it difficult to share data across Member States; the EHDS, based on several regulations, provides tailor-made rules…
European Commission Amends Digital Europe Programme With an Investment of €762.7 Million
The European Commission has amended the Digital Europe Programme work programmes 2023-2024, investing an additional €762.7 million in Europe’s digital transition and cybersecurity. The digital transition is the main work programme with a budget of…
SPMS Integrates the TEF-Health Initiative
SPMS participates in the TEF-Health initiative as a partner in a consortium composed of 51 entities from 9 European Union countries. This action is co-financed by the European Commission and has a duration of five…
FMUP Creates Inhealth Junior Academy for High School Students
The InHealth Junior Academy — Academia Júnior de Inovação em Saúde is an initiative of the Departamento de Medicina da Comunidade, Informação e Decisão em Saúde da Faculdade de Medicina da Universidade do Porto (FMUP)….
SPMS Represents Portugal as Vice-president of GDHP
The GDHP is an intergovernmental organization in the digital health sector that facilitates cooperation and collaboration between government representatives and the World Health Organization (WHO). Its purpose is to foster policymaking that promote the digitalization…
Digital Transformation of Health at INCoDe.2030 in Tomar
The “National Digital Skills Initiative e.2030, Portugal” (INCoDe.2030) is an initiative that aims to improve the Portuguese population’s level of digital skills, placing Portugal at the level of the most advanced European countries in this…
Braga Hospital Evaluates Memory With Interactive Game in Patients With Multiple Sclerosis
Multiple Sclerosis is known as a chronic disease of the central nervous system, with a wide variety of motor and sensory symptoms that can lead to work disability, socioeconomic burden, and reduced quality of life…
Neurosurgery Teleconsultation Wins Innovation Award
The aim of the BI Award for Innovation in Healthcare is to recognize innovative projects in the healthcare sector that improve the quality of life for the Portuguese people. In 2021, the specific theme was…
HealthData@PT: New SPMS Initiative for Health Data
Action HealthData@PT is launched in the context of the implementation of the European Health Data Space, and is an initiative approved by the European Commission under the EU4Health 2021-2027 programme. This initiative contributes to the…
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