Bridging the gap between AI and Healthcare sides: towards developing clinically relevant AI-powered diagnosis systems

• URL: http://arxiv.org/abs/2001.03923v2
• Date: Mon, 6 Apr 2020 16:24:51 GMT
• Title: Bridging the gap between AI and Healthcare sides: towards developing clinically relevant AI-powered diagnosis systems
• Authors: Changhee Han, Leonardo Rundo, Kohei Murao, Takafumi Nemoto, Hideki Nakayama
• Abstract summary: We hold a clinically valuable AI-envisioning workshop among Japanese Medical Imaging experts, physicians, and generalists in Healthcare/Informatics. Then, a questionnaire survey for physicians evaluates our pathology-aware Generative Adrial Network (GAN)-based image augmentation projects in terms of Data Augmentation and physician training.
• Score: 18.95904791202457
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the success of Convolutional Neural Network-based Computer-Aided Diagnosis research, its clinical applications remain challenging. Accordingly, developing medical Artificial Intelligence (AI) fitting into a clinical environment requires identifying/bridging the gap between AI and Healthcare sides. Since the biggest problem in Medical Imaging lies in data paucity, confirming the clinical relevance for diagnosis of research-proven image augmentation techniques is essential. Therefore, we hold a clinically valuable AI-envisioning workshop among Japanese Medical Imaging experts, physicians, and generalists in Healthcare/Informatics. Then, a questionnaire survey for physicians evaluates our pathology-aware Generative Adversarial Network (GAN)-based image augmentation projects in terms of Data Augmentation and physician training. The workshop reveals the intrinsic gap between AI/Healthcare sides and solutions on Why (i.e., clinical significance/interpretation) and How (i.e., data acquisition, commercial deployment, and safety/feeling safe). This analysis confirms our pathology-aware GANs' clinical relevance as a clinical decision support system and non-expert physician training tool. Our findings would play a key role in connecting inter-disciplinary research and clinical applications, not limited to the Japanese medical context and pathology-aware GANs.

Related papers

• Transforming Dental Diagnostics with Artificial Intelligence: Advanced Integration of ChatGPT and Large Language Models for Patient Care [0.196629787330046]
  This study delves into the impact of cutting-edge Large Language Models (LLMs) on medical diagnostics, with a keen focus on the dental sector. The advent of ChatGPT-4 is poised to make substantial inroads into dental practices, especially in the realm of oral surgery. It critically assesses the broad implications and challenges within various sectors, including academia and healthcare.
  arXiv  Detail & Related papers  (2024-06-07T06:44:09Z)
• 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)
• Practical Applications of Advanced Cloud Services and Generative AI Systems in Medical Image Analysis [17.4235794108467]
  The article explores the transformative potential of generative AI in medical imaging, emphasizing its ability to generate syntheticACM-2 data. By addressing limitations in dataset size and diversity, these models contribute to more accurate diagnoses and improved patient outcomes.
  arXiv  Detail & Related papers  (2024-03-26T09:55:49Z)
• MedKP: Medical Dialogue with Knowledge Enhancement and Clinical Pathway Encoding [48.348511646407026]
  We introduce the Medical dialogue with Knowledge enhancement and clinical Pathway encoding framework. The framework integrates an external knowledge enhancement module through a medical knowledge graph and an internal clinical pathway encoding via medical entities and physician actions.
  arXiv  Detail & Related papers  (2024-03-11T10:57:45Z)
• AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator [69.51568871044454]
  We introduce textbfAI Hospital, a framework simulating dynamic medical interactions between emphDoctor as player and NPCs. This setup allows for realistic assessments of LLMs in clinical scenarios. We develop the Multi-View Medical Evaluation benchmark, utilizing high-quality Chinese medical records and NPCs.
  arXiv  Detail & Related papers  (2024-02-15T06:46:48Z)
• Current State of Community-Driven Radiological AI Deployment in Medical Imaging [1.474525456020066]
  This report represents several years of weekly discussions and hands-on problem solving experience by groups of industry experts and clinicians in the MONAI Consortium. We identify barriers between AI-model development in research labs and subsequent clinical deployment. We discuss various AI integration points in a clinical Radiology workflow.
  arXiv  Detail & Related papers  (2022-12-29T05:17:59Z)
• A Trustworthy Framework for Medical Image Analysis with Deep Learning [71.48204494889505]
  TRUDLMIA is a trustworthy deep learning framework for medical image analysis. It is anticipated that the framework will support researchers and clinicians in advancing the use of deep learning for dealing with public health crises including COVID-19.
  arXiv  Detail & Related papers  (2022-12-06T05:30:22Z)
• Towards the Use of Saliency Maps for Explaining Low-Quality Electrocardiograms to End Users [45.62380752173638]
  When using medical images for diagnosis, it is important that the images are of high quality. In telemedicine, a common problem is that the quality issue is only flagged once the patient has left the clinic, meaning they must return in order to have the exam redone. This paper reports on the development of an AI system for flagging and explaining low-quality medical images in real-time.
  arXiv  Detail & Related papers  (2022-07-06T14:53:26Z)
• Survey of XAI in digital pathology [3.4591414173342643]
  We present a survey on XAI within digital pathology, a medical imaging sub-discipline with particular characteristics and needs. We give a thorough overview of current XAI techniques of potential relevance for deep learning methods in pathology imaging. In doing, we incorporate uncertainty estimation methods as an integral part of the XAI landscape.
  arXiv  Detail & Related papers  (2020-08-14T13:11:54Z)
• Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19 [71.41929762209328]
  The pandemic of coronavirus disease 2019 (COVID-19) is spreading all over the world. Medical imaging such as X-ray and computed tomography (CT) plays an essential role in the global fight against COVID-19. The recently emerging artificial intelligence (AI) technologies further strengthen the power of the imaging tools and help medical specialists.
  arXiv  Detail & Related papers  (2020-04-06T15:21:34Z)

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.