From Sights to Insights: Towards Summarization of Multimodal Clinical Documents (2024)

Abstract: The advancement of Artificial Intelligence is pivotal in reshaping healthcare, enhancing diagnostic precision, and facilitating personalized treatment strategies. One major challenge for healthcare professionals is quickly navigating through long clinical documents to provide timely and effective solutions. Doctors often struggle to draw quick conclusions from these extensive documents. To address this issue and save time for healthcare professionals, an effective summarization model is essential. Most current models assume the data is only text-based. However, patients often include images of their medical conditions in clinical documents. To effectively summarize these multimodal documents, we introduce , an innovative Image-Guided Encoder-Decoder Model. This model uses modality-aware contextual attention on the encoder and an image cross-attention mechanism on the decoder, enhancing the BART base model to create detailed visual-guided summaries. We have tested our model extensively on three multimodal clinical benchmarks involving multimodal question and dialogue summarization tasks. Our analysis demonstrates that outperforms state-of-the-art large language and vision-aware models in these summarization tasks. Disclaimer : The work includes vivid medical illustrations, depicting the essential aspects of the subject matter.

## introduction one of the most impactful sectors where ai advancements can bring a significant revolution is healthcare. the latest who report highlights a drastic doctor-to-patient ratio, emphasizing the need for automation in various healthcare tasks. this imbalance, coupled with strides in information and communication technologies (icts) and the impact of covid-19, has led to a significant rise in telehealth practices @xcite . recent studies @xcite , @xcite , @xcite , @xcite demonstrate that ai models can be effectively employed in various healthcare settings and can drastically increase the productivity of healthcare professionals. in this regard, a significant development is that a considerable number of patients now discuss their medical history and conditions in various clinical forums before attending in-person appointments. distilling key insights from these clinical documents not only boosts the efficiency of ai-based healthcare systems but also accelerates responses to patients, providing valuable time savings for healthcare professionals and facilitating a more prompt and beneficial exchange of information between both patients and doctors @xcite . most research in the field of medical document summarization has focused on unimodal, textbased approaches. @xcite explored medical query summarization using entity-driven contrastive learning. similarly, @xcite ) addressed text-based medical dialogue summarization, while @xcite focused on summarizing multiple medical documents. recognizing the adage that an image is worth a thousand words, a discernible shift is underway, with patients increasingly incorporating images of their medical conditions alongside their queries. this inclination arises from the fact that a considerable portion of the population may not be well-versed in medical terminology. this challenge becomes more pronounced when dealing with related symptoms, such as distinguishing between eye redness and eye inflammation. integrating text and images provides a more comprehensive context, enriching the analysis beyond what textual examination alone might capture, which is underscored in the work @xcite . motivated by the research gap @xcite , @xcite , and @xcite have contributed datasets along with their proposed models, utilizing techniques such as prompting and end-to-end finetuning to incorporate visual information alongside textual content. each of these works has established its own model, but no single model has been developed that is well-suited for all these multimodal clinical summarization benchmark datasets. our exploration culminates in proposing a model named edi-summ, designed to work across all the mentioned multimodal clinical summarization datasets. edi-summ integrates contextual multimodal fusion within the encoder and employs a cross-attention mechanism in the decoder of the bart architecture. ## methodology this section delineates the problem statement and outlines the various components of our proposed model. ## experimental results and analysis the following section outlines the datasets used for analysis, experimental setup, and evaluation discussion of the performance of the proposed model across a spectrum of both automatic and human evaluation metrics. a qualitative analysis of the generated summaries is conducted under various model configurations. ## risk analysis through comprehensive automated, human, and qualitative analyses, we have discerned that visual cues significantly contribute to generating more clinically nuanced summaries. however, particularly in sectors as critical as healthcare, the stakes are high for any potential misinformation or misinterpretation. in our human evaluation, instances were identified where the model, due to visual information, missed crucial keywords from the textual query. this occasional divergence underscores the need to carefully balance visual and textual inputs to ensure a more comprehensive summary. moreover, our model is currently limited to recognizing 18 main symptoms. if presented with an image con-taining symptoms outside this set, the model might inadvertently generate misinformation in the final summary. additionally, the quality of the image plays a pivotal role in generating accurate summaries. our experimentation with a low-quality image revealed a lack of proper detail capture. hence, the involvement of a medical expert becomes im perative, particularly in high stakes, to mitigate the risks associated with potential inaccuracies or oversights. ## conclusion in this paper, we introduced a multimodal model edi-summ, which incorporates contextual multimodal attention in both the encoder and decoder of the bart model. to evaluate the effectiveness of our proposed model, we conducted automated and human evaluations on three clinical multimodal summarization benchmark datasets. our analysis suggests that incorporating contextual attention in the encoder and cross-attention in the decoder enhances the performance of multimodal summarization.