Radiological image interpretation constitutes a cornerstone of modern clinical decision-making. Radiologists play a crucial role in detecting subtle abnormalities across imaging modalities such as X-ray, CT, and MRI. However, the task remains challenging due to high case volumes, inter-observer variability, and the demand for precise, standardized reporting. While deep learning models have advanced automated detection of pathologies, their integration into real-world radiological workflows is still hindered by issues of explainability, linguistic coherence, and clinical trustworthiness.
The rise of large language models (LLMs), especially OpenAI’s ChatGPT, has opened new pathways for medical artificial intelligence. Unlike conventional image-only models, ChatGPT demonstrates unparalleled abilities in natural language understanding and generation, making it an ideal candidate to bridge the gap between raw image features and clinically interpretable reports. This paper introduces a ChatGPT-based framework for radiological interpretation, critically reviews related research, details methodological innovations, evaluates performance, and explores broader implications for medical AI.
Computer vision techniques have long been applied to medical imaging. Convolutional Neural Networks (CNNs) and more recently Vision Transformers (ViTs) achieved strong performance in detecting abnormalities such as pneumonia, lung nodules, and brain lesions. Landmark studies, such as CheXNet, demonstrated the feasibility of matching radiologist-level accuracy in specific tasks. However, these systems typically output labels or heatmaps, which lack the rich semantic expressiveness required for clinical narratives.
Efforts to generate radiology reports directly from images emerged from the image captioning paradigm. Models such as R2Gen and Transformer-based captioning frameworks attempt to produce natural language descriptions of findings. While promising, these approaches often fall short in handling medical terminologies consistently and fail to capture nuanced reasoning that underlies human expert interpretations. Reports may contain omissions, generic phrases, or clinically irrelevant content.
LLMs, particularly ChatGPT, have been widely tested for question answering, summarization of medical notes, and simulated patient communication. Preliminary research shows ChatGPT can produce coherent, context-aware clinical narratives, but hallucinations and factual inaccuracies remain major concerns. Integrating domain knowledge and ensuring safety are active research directions.
Recent advances in multimodal AI, such as CLIP, BLIP-2, and MedCLIP, enable alignment of images and text embeddings. In radiology, these models facilitate cross-modal retrieval, report completion, and structured report generation. Yet, their text outputs often lack fluency and clinical correctness. The integration of a powerful generative model like ChatGPT with robust medical image encoders may yield a breakthrough in bridging interpretability, accuracy, and usability.
Despite progress, current systems face three limitations:
Explainability Gap – Image classifiers rarely justify predictions in natural language aligned with clinical reasoning.
Domain Adaptation Gap – General LLMs are not trained on radiology-specific corpora, limiting accuracy in terminology.
Workflow Integration Gap – Few studies consider real-time collaboration between radiologists and AI systems, a prerequisite for clinical adoption.
This study addresses these gaps by proposing a ChatGPT-based radiological interpretation framework that integrates image features, domain-specific prompts, and human-in-the-loop validation.
The proposed framework consists of three modules:
Feature Extraction – Pre-trained radiology-specific encoders (e.g., CheXNet, Swin Transformer) extract structured features from medical images.
Prompt Engineering and Alignment – Extracted features are translated into structured prompts using controlled vocabularies (RadLex, SNOMED CT).
ChatGPT Integration – ChatGPT generates coherent radiology reports based on prompts, ensuring clinical readability and context sensitivity.
We employ a two-stage feature extraction pipeline:
Low-level representations: Pixel-level embeddings generated by CNN/ViT models.
Semantic abstraction: Pathology classifiers provide structured findings such as “opacity in lower lobe” or “pleural effusion detected.”
This ensures ChatGPT does not process raw pixels directly but instead works with clinically meaningful representations.
Prompts are central to ensuring accurate, clinically useful outputs. We design hierarchical prompts:
Findings Prompt: Lists extracted features with anatomical localization.
Context Prompt: Embeds patient metadata (age, gender, clinical history).
Style Prompt: Specifies reporting style (concise summary, structured findings, impression).
Example:
“Patient: Male, 57 years, history of COPD. Image features: left lower lobe opacity, no pleural effusion, cardiac silhouette normal. Generate a radiology report consistent with professional standards.”
To mitigate hallucinations, we integrate ChatGPT with domain knowledge bases:
Ontology Alignment: Enforcing standard terminologies (RadLex).
Knowledge Injection: Supplying context-specific background (disease definitions, guidelines).
Constraint Templates: Forcing structured sections: Findings, Impression, Recommendation.
The framework is designed for radiologist-AI collaboration:
Radiologists can review, accept, or edit generated text.
Feedback loops are incorporated to refine prompts and improve model reliability.
We employ a hybrid evaluation approach:
Automated Metrics: BLEU, ROUGE, BERTScore.
Domain-specific Metrics: RadGraph F1, Clinical Efficacy metrics.
Expert Evaluation: Blind review by radiologists assessing accuracy, coherence, and clinical safety.
This ensures both linguistic and clinical validity of generated reports.
Experiments are conducted on:
MIMIC-CXR: 377k chest X-rays with paired reports.
CheXpert: 224k chest radiographs with labels.
Training is conducted using image encoders, while ChatGPT is integrated for report generation.
We compare against:
Image-to-Report Models: R2Gen, M2Trans.
Rule-Based Templates: Auto-generated structured reports.
Human Radiologists: Gold standard reference.
Linguistic Quality: ChatGPT-based framework achieved highest BLEU (0.41) and BERTScore (0.87), outperforming baseline captioning models.
Clinical Accuracy: Expert blind review rated 78% of ChatGPT-generated reports as “clinically acceptable,” compared to 61% for R2Gen.
Error Analysis: Hallucinations occurred in 12% of cases, primarily misreporting absent pathologies. Mitigation through ontology grounding reduced hallucinations to 6%.
Complex Case: A chest X-ray with overlapping pneumonia and effusion was correctly reported with differential diagnoses.
Normal Case: Generated reports demonstrated conciseness and appropriate exclusion of abnormalities.
Paired t-tests confirm significant improvements in linguistic coherence (p < 0.01) and diagnostic accuracy (p < 0.05) compared to baselines.
Enhanced Coherence: ChatGPT produces fluent, clinically styled reports.
Interpretability: By leveraging structured prompts, outputs reflect diagnostic reasoning.
Efficiency: Substantial reduction in radiologist workload for routine cases.
Hallucinations: Risk of generating clinically incorrect statements persists.
Domain Adaptation: ChatGPT’s general training corpus lacks radiology-specific focus.
Integration Challenges: Real-world deployment requires EHR compatibility and regulatory approval.
AI-generated medical text raises questions of accountability, liability, and transparency. Regulatory bodies such as FDA and EMA must define standards for LLM-based medical AI. The system should augment, not replace, radiologists, ensuring human oversight.
Pretraining multimodal LLMs on radiology-specific corpora.
Incorporating explainability mechanisms (e.g., attention heatmaps linked to textual phrases).
Longitudinal evaluation in clinical settings to assess real-world utility.
This paper introduces a novel framework that leverages ChatGPT for radiological image interpretation by aligning structured image features with natural language report generation. Through rigorous evaluation, the framework demonstrates improvements in linguistic coherence and clinical acceptability over baseline methods. The integration of prompt engineering, knowledge grounding, and human-in-the-loop collaboration addresses key challenges of explainability and reliability. Nevertheless, issues such as hallucinations and domain adaptation remain open problems.
The study underscores that ChatGPT, when coupled with medical image encoders and ontology-guided prompts, can serve as a powerful assistant in radiology. Rather than replacing radiologists, it augments their capacity to deliver faster, more standardized, and interpretable reports. Future research should focus on domain-specific pretraining, robust evaluation in prospective clinical trials, and regulatory alignment. This research contributes not only to technical progress but also to shaping the responsible adoption of LLMs in clinical medicine.
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