Are Generative AI systems Capable of Supporting Information Needs of Patients?

• URL: http://arxiv.org/abs/2402.00234v1
• Date: Wed, 31 Jan 2024 23:24:37 GMT
• Title: Are Generative AI systems Capable of Supporting Information Needs of Patients?
• Authors: Shreya Rajagopal, Subhashis Hazarika, Sookyung Kim, Yan-ming Chiou, Jae Ho Sohn, Hari Subramonyam, Shiwali Mohan
• Abstract summary: We investigate whether and how generative visual question answering systems can responsibly support patient information needs in the context of radiology imaging data. We conducted a formative need-finding study in which participants discussed chest computed tomography (CT) scans and associated radiology reports of a fictitious close relative with a cardiothoracic radiologist. Using thematic analysis of the conversation between participants and medical experts, we identified commonly occurring themes across interactions. We evaluate two state-of-the-art generative visual language models against the radiologist's responses.
• Score: 4.485098382568721
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Patients managing a complex illness such as cancer face a complex information challenge where they not only must learn about their illness but also how to manage it. Close interaction with healthcare experts (radiologists, oncologists) can improve patient learning and thereby, their disease outcome. However, this approach is resource intensive and takes expert time away from other critical tasks. Given the recent advancements in Generative AI models aimed at improving the healthcare system, our work investigates whether and how generative visual question answering systems can responsibly support patient information needs in the context of radiology imaging data. We conducted a formative need-finding study in which participants discussed chest computed tomography (CT) scans and associated radiology reports of a fictitious close relative with a cardiothoracic radiologist. Using thematic analysis of the conversation between participants and medical experts, we identified commonly occurring themes across interactions, including clarifying medical terminology, locating the problems mentioned in the report in the scanned image, understanding disease prognosis, discussing the next diagnostic steps, and comparing treatment options. Based on these themes, we evaluated two state-of-the-art generative visual language models against the radiologist's responses. Our results reveal variability in the quality of responses generated by the models across various themes. We highlight the importance of patient-facing generative AI systems to accommodate a diverse range of conversational themes, catering to the real-world informational needs of patients.

Related papers

• D-Rax: Domain-specific Radiologic assistant leveraging multi-modal data and eXpert model predictions [8.874099055563228]
  We propose D-Rax -- a domain-specific, conversational, radiologic assistance tool. We enhance the conversational analysis of chest X-ray (CXR) images to support radiological reporting. We observe statistically significant improvement in responses when evaluated for both open and close-ended conversations.
  arXiv  Detail & Related papers  (2024-07-02T18:43:10Z)
• Dr-LLaVA: Visual Instruction Tuning with Symbolic Clinical Grounding [53.629132242389716]
  Vision-Language Models (VLM) can support clinicians by analyzing medical images and engaging in natural language interactions. VLMs often exhibit "hallucinogenic" behavior, generating textual outputs not grounded in contextual multimodal information. We propose a new alignment algorithm that uses symbolic representations of clinical reasoning to ground VLMs in medical knowledge.
  arXiv  Detail & Related papers  (2024-05-29T23:19:28Z)
• Towards Knowledge-Infused Automated Disease Diagnosis Assistant [14.150224660741939]
  We build a diagnosis assistant to assist doctors, which identifies diseases based on patient-doctor interaction. We propose a two-channel, discourse-aware disease diagnosis model (KI-DDI), where the first channel encodes patient-doctor communication. In the next stage, the conversation and knowledge graph embeddings are infused together and fed to a deep neural network for disease identification.
  arXiv  Detail & Related papers  (2024-05-18T05:18:50Z)
• Estimating the severity of dental and oral problems via sentiment classification over clinical reports [0.8287206589886879]
  Analyzing authors' sentiments in texts can be practical and useful in various fields, including medicine and dentistry. Deep learning model based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network architecture, known as CNN-LSTM, was developed to detect severity level of patient's problem.
  arXiv  Detail & Related papers  (2024-01-17T14:33:13Z)
• Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A review [2.2268038840298714]
  We review the state-of-the-art machine learning studies that adopted the fusion of biomedical knowledge and data. We provide an overview of diverse forms of knowledge representation and current strategies of knowledge integration into machine learning pipelines.
  arXiv  Detail & Related papers  (2024-01-12T07:01:36Z)
• Radiology Report Generation Using Transformers Conditioned with Non-imaging Data [55.17268696112258]
  This paper proposes a novel multi-modal transformer network that integrates chest x-ray (CXR) images and associated patient demographic information. The proposed network uses a convolutional neural network to extract visual features from CXRs and a transformer-based encoder-decoder network that combines the visual features with semantic text embeddings of patient demographic information.
  arXiv  Detail & Related papers  (2023-11-18T14:52:26Z)
• XrayGPT: Chest Radiographs Summarization using Medical Vision-Language Models [60.437091462613544]
  We introduce XrayGPT, a novel conversational medical vision-language model. It can analyze and answer open-ended questions about chest radiographs. We generate 217k interactive and high-quality summaries from free-text radiology reports.
  arXiv  Detail & Related papers  (2023-06-13T17:59:59Z)
• Medical Image Captioning via Generative Pretrained Transformers [57.308920993032274]
  We combine two language models, the Show-Attend-Tell and the GPT-3, to generate comprehensive and descriptive radiology records. The proposed model is tested on two medical datasets, the Open-I, MIMIC-CXR, and the general-purpose MS-COCO.
  arXiv  Detail & Related papers  (2022-09-28T10:27:10Z)
• MIMO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning [49.57261599776167]
  We propose an end-to-end robust Transformer-based solution, Mutual Integration of patient journey and Medical Ontology (MIMO) for healthcare representation learning and predictive analytics.
  arXiv  Detail & Related papers  (2021-07-20T07:04:52Z)
• MedDG: An Entity-Centric Medical Consultation Dataset for Entity-Aware Medical Dialogue Generation [86.38736781043109]
  We build and release a large-scale high-quality Medical Dialogue dataset related to 12 types of common Gastrointestinal diseases named MedDG. We propose two kinds of medical dialogue tasks based on MedDG dataset. One is the next entity prediction and the other is the doctor response generation. Experimental results show that the pre-train language models and other baselines struggle on both tasks with poor performance in our dataset.
  arXiv  Detail & Related papers  (2020-10-15T03:34:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.