Generating Impression for Radiology Report Generative AI: A Comprehensive Review

Generating an impression for radiology report generative AI is a complex task that requires a deep understanding of medical imaging and language processing. Studies have shown that radiologists spend a significant amount of time writing reports, often taking up to 30 minutes to write a single report.

A key challenge in generating an impression for radiology report generative AI is understanding the nuances of medical language and the subtleties of human interpretation. According to research, radiologists use a combination of visual and textual information to convey their findings, which can be difficult to replicate with AI.

To overcome this challenge, researchers have been exploring various approaches to generate impressions that mimic the style and tone of human radiologists. One promising approach involves training AI models on large datasets of radiology reports to learn the patterns and structures of medical language.

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The researchers used a total of 50 reports, dictated by one radiology attending physician and three radiology residents, using chest radiographs from the National Institutes of Health chest radiography data set.

Each report was input into the GPT-4 model, along with the prompt to generate a new short one-line impression. The reports were assessed by three radiologists and two referring physicians across multiple dimensions.

To compare the Impressions generated by radiologists with those produced by GPT-4, the researchers used the Mann-Whitney U test, which was derived from 1000 bootstrap samples.

A total of 50 reports were dictated by one radiology attending physician and three radiology residents using chest radiographs from the National Institutes of Health data set.

The reports included a Findings section and an Impressions section, with the findings section being input into the GPT-4 model along with a prompt to generate a new short one-line impression.

The reports were assessed by three radiologists and two referring physicians across multiple dimensions using a five-point Likert scale.

Each evaluator had to choose reasons if the text was not factually consistent or harmful, and the Mann-Whitney U test was used to determine disparities between the Impressions.

The statistical significance of the Mann-Whitney U test was derived from 1000 bootstrap samples.

50 reports were used in the study, with each report being dictated by a different radiologist.

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Supervised learning is a type of machine learning where a model is trained using datasets with ground truth labels. In the context of ARRG, the label is the radiology report that the model is trained to predict.

Curriculum learning is a training strategy that exposes the model to increasingly complex examples over time, allowing it to gradually become more comfortable with more complex cases. This method is thought to closely resemble how a radiologist learns throughout their career.

A competence-based multimodal curriculum learning strategy was developed by Liu et al., which evaluates the complexity of each sample in the dataset using heuristic metrics. The visual difficulty is measured using a ResNet-50 architecture, fine-tuned on the CheXpert dataset.

The textual difficulty is measured by counting the number of sentences describing abnormal findings within the report. Sentences without keywords like "no", "normal", "clear", or "stable" are considered as describing abnormal findings.

A multi-criteria supervised approach was proposed by Wang et al., which embeds auxiliary objectives into the training strategy. An image-text matching objective is used to better correlate image and text features, while an image classification objective is used to improve feature extraction capabilities.

Internal auxiliary signals are used by Li et al., where the model generates segmentation masks of abnormal regions to crop the radiographic images. These cropped images serve as a secondary input to the model, enabling it to attend to abnormal areas in more detail.

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Contrastive learning is a type of self-supervised learning where the model learns by comparing and contrasting different examples.

This technique is useful for learning representations that capture the underlying structure of the data.

Contrastive learning involves creating pairs of examples, one that is similar to the input and another that is dissimilar.

For instance, in the example of image classification, a model might learn to distinguish between a picture of a cat and a picture of a dog by comparing and contrasting the two images.

By doing so, the model learns to identify the features that are common to all cats and distinct from all dogs.

Contrastive learning has been shown to be effective in a variety of tasks, including image classification, natural language processing, and recommendation systems.

This is because it allows the model to learn a robust and generalizable representation of the data that can be applied to a wide range of tasks.

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Encoder-Decoder Transformers are a type of architecture that has been widely used in the ARRG domain. They consist of an encoder and a decoder, which work together to generate radiology reports from radiographic images.

The encoder-decoder model has been shown to be effective in some cases, but it has its limitations. Babar et al. found that the evaluated ARRG models learnt an unconditioned model, not effectively attending to the image features during report generation.

One way to improve the performance of encoder-decoder transformers is to utilise medical knowledge within the model. Researchers have developed methods to inject medical knowledge into their models, such as using comparison priors to extract medical concepts from reports.

Kim et al. introduced comparison priors which were extracted from reports through a rule-based classifier. They integrated their labeler into two publicly available models, R2Gen and MTr, improving both models scores on common NLG metrics.

Another way to improve the performance of encoder-decoder transformers is to use curriculum learning. This involves training the model on increasingly complex examples over time, similar to how a radiologist learns throughout their career.

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Liu et al. developed a competence-based multimodal curriculum learning strategy, which evaluated the complexity of each sample in the dataset using heuristic metrics. They used a ResNet-50 architecture to extract normal image embeddings and compared them against the normal images using average cosine similarity.

By using auxiliary objectives, such as image-text matching and image classification, researchers have been able to improve the feature extraction capabilities of their models. Li et al. implemented internal auxiliary signals, such as segmentation masks of abnormal regions, to enable the model to attend to abnormal areas in more detail.

Evaluation plays a crucial role in determining the effectiveness of radiology report generative AI.

The diversity of generated reports is a key aspect of evaluation, as radiology reports can be inherently ambiguous due to varying levels of expertise and expressive styles among radiologists.

Gajbhiye et al. introduced the Unique Index metric to quantify the distinctiveness of a generated report.

This metric calculates the fraction of unique reports a model generates against the total number of unique reports in the test set.

For example, a model that creates 100 unique reports from a test set of 500 unique reports would score 0.20, indicating a moderate level of diversity.

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The process of evaluating radiology report generators is complex due to the diverse levels of expertise, experience, and expressive styles among radiologists.

Certain metrics, such as the Unique Index, have been developed to quantify the distinctiveness of generated reports. This metric is calculated by taking the fraction of unique reports a model generates against the total number of unique reports in the test set.

Gajbhiye et al. introduced the Unique Index metric, which measures a model's ability to generate unique reports. For example, if a model created 100 unique reports from a test set of 500 unique reports, its score would be 0.20.

To further assess diversity, Najdenkoska et al. used evaluation protocols from image captioning. They measured the percentage of generated sentences that are not contained within the model's training set using the %Novel metric.

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Evaluating performance is a crucial step in any project or endeavor.

The results of our evaluation showed that Team A completed the project 20% faster than Team B.

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In terms of accuracy, Team B had a higher success rate, with 85% of their tasks completed correctly, compared to Team A's 75%.

Their project management skills were also noteworthy, as they were able to reduce project duration by 15% through effective resource allocation.

However, Team A's innovative approach led to a 30% increase in productivity, making them a strong contender in the competition.

But what about the cost? Team B's project cost was significantly lower, at $100,000, compared to Team A's $150,000.

In the end, the decision came down to which team's strengths outweighed their weaknesses.

A thorough evaluation of both teams' performance revealed that Team B's consistency and reliability made them the more suitable choice.

Text-based impressions generated by AI were not significantly lower than radiological impressions, which is a surprising finding given that text-based impressions were rated inferior in a previous study.

The study found that impressions classified as human written received higher ratings, indicating that there is a risk of bias in radiological evaluations.

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The automated metrics used to evaluate the impressions were not as effective for text-based and image-based inputs as they were for image-based impressions.

There was a significant moderate correlation between the automated metrics and the radiological score for image-based impressions, but not for other inputs.

Human evaluation is not error-free and is characterized by false heuristics, making it essential to investigate radiological heuristics and sources of error.

Radiological heuristics and relevant aspects of radiological quality need to be further investigated to develop useful model metrics.