Radiology has long relied on expert annotations to enrich medical imaging data for research and training artificial intelligence systems. These annotations, created by specialists, have traditionally been applied manually—an effort-intensive process that limits scalability. As the demand for large-scale, labelled datasets grows, so does the need for more efficient annotation methods. The emergence of large language models (LLMs) has introduced a transformative approach, allowing for the automated extraction and normalisation of image labels directly from clinical reports. This shift from manual to semiautomated annotation represents a significant milestone in radiology's ongoing digital evolution.
Traditional Annotation Challenges
Conventional radiology annotation practices are slow, resource-heavy and difficult to scale. The process typically begins after clinical decisions have been made, requiring recruitment and training of domain experts to apply a bespoke annotation schema. Depending on the project, annotations may be created at varying levels—from entire examinations to individual image pixels—requiring meticulous quality control to maintain consistency and accuracy. However, annotations created for one study often lack reusability for others due to divergent aims, variable labelling conventions and inconsistent reporting styles. Although some platforms support web-based annotation with standardised output formats like JSON or DICOM, lack of universal syntactic and semantic standards hampers interoperability. Attempts to establish shared frameworks, such as the AIM initiative, have seen limited uptake. As a result, annotations generated in clinical systems are seldom reused in AI training workflows.
Automated Label Generation with NLP and LLMs
Efforts to streamline annotation have increasingly turned to natural language processing (NLP) tools, which can extract key findings from radiology reports and repurpose them as labels. Early rule-based NLP methods enabled basic concept extraction, but variability in reporting styles posed challenges. The advent of transformer-based models, notably BERT and its derivatives, brought substantial improvements in accuracy, with domain-specific variants like BioBERT and CheXbert outperforming traditional models. These advances demonstrated that automated labelling from clinical reports was both feasible and scalable. Fine-tuning pretrained models on small expert-labelled datasets has proven effective, reducing the need for extensive new annotations while maintaining high performance across a range of diagnostic tasks. Nevertheless, even these models require upfront training tailored to specific goals and datasets.
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LLMs have further advanced this capability by offering general-purpose models that can be guided through prompt engineering rather than retrained for each use case. Their ability to map report content to standard vocabularies, identify inconsistencies and extract relevant features makes them especially valuable for large-scale label generation. Studies have shown that LLM-generated labels from clinical reports can match or surpass the accuracy of earlier methods, with applications in error detection, summary generation and classification of imaging findings. The iterative nature of prompt refinement and the use of ensemble models or random audits helps ensure label quality while reducing manual workload. These developments signal a paradigm shift in how radiological labels are derived and validated.
Toward Multimodal and Self-Supervised Annotation
The future of annotation lies in combining LLMs with visual models to achieve fully automated, multimodal labelling. While LLMs excel at extracting concepts from text, they cannot directly annotate pixels or define regions of interest. This gap is being addressed through helper AI systems that analyse pixel data to generate metadata such as imaging modality, body part and contrast phase. Tools like Segment Anything and MedSam are capable of producing accurate segmentations without human input, enabling cross-institutional standardisation despite local variation in naming conventions.
In parallel, self-supervised learning techniques such as contrastive learning allow models to train on vast unlabeled datasets by identifying similarities and differences in image pairs. When paired with clinical report data, these models can encode the relationship between textual and visual features, allowing them to generate pixel-level annotations automatically. Examples like AFloc and LiteGPT have demonstrated the viability of this approach in radiography. If successfully extended to more complex modalities, these methods could dramatically reduce the annotation burden, relegating human experts to auditing a small sample of automated outputs. This convergence of multimodal AI and contrastive learning presents a compelling path toward scalable, standardised and low-cost annotation of medical images.
With the movement toward foundational visual models, the role of annotation in radiology is being redefined. Manual labelling remains too labour-intensive for the growing demand of training data, prompting the adoption of LLMs and other semiautomated techniques to generate structured labels from clinical reports. These approaches not only expedite dataset preparation but also enable consistent use of standard vocabularies across diverse reporting styles. While human oversight remains essential to ensure quality and accuracy, future systems will rely increasingly on LLMs, helper AI tools and self-supervised learning to automate the bulk of annotation work. This transformation allows human expertise to be focused on higher-level validation, driving greater efficiency and scalability in medical imaging AI development.
Source: Radiology: Artificial Intelligence
Image Credit: iStock
