Enabling Collaborative Clinical Diagnosis of Infectious Keratitis by Integrating Expert Knowledge and Interpretable Data-driven Intelligence

• URL: http://arxiv.org/abs/2401.08695v1
• Date: Sun, 14 Jan 2024 02:10:54 GMT
• Title: Enabling Collaborative Clinical Diagnosis of Infectious Keratitis by Integrating Expert Knowledge and Interpretable Data-driven Intelligence
• Authors: Zhengqing Fang, Shuowen Zhou, Zhouhang Yuan, Yuxuan Si, Mengze Li, Jinxu Li, Yesheng Xu, Wenjia Xie, Kun Kuang, Yingming Li, Fei Wu, and Yu-Feng Yao
• Abstract summary: This study investigates the performance, interpretability, and clinical utility of knowledge-guided diagnosis model (KGDM) in the diagnosis of infectious keratitis (IK) The diagnostic odds ratios (DOR) of the interpreted AI-based biomarkers are effective, ranging from 3.011 to 35.233. The participants with collaboration achieved a performance exceeding that of both humans and AI.
• Score: 28.144658552047975
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Although data-driven artificial intelligence (AI) in medical image diagnosis has shown impressive performance in silico, the lack of interpretability makes it difficult to incorporate the "black box" into clinicians' workflows. To make the diagnostic patterns learned from data understandable by clinicians, we develop an interpretable model, knowledge-guided diagnosis model (KGDM), that provides a visualized reasoning process containing AI-based biomarkers and retrieved cases that with the same diagnostic patterns. It embraces clinicians' prompts into the interpreted reasoning through human-AI interaction, leading to potentially enhanced safety and more accurate predictions. This study investigates the performance, interpretability, and clinical utility of KGDM in the diagnosis of infectious keratitis (IK), which is the leading cause of corneal blindness. The classification performance of KGDM is evaluated on a prospective validation dataset, an external testing dataset, and an publicly available testing dataset. The diagnostic odds ratios (DOR) of the interpreted AI-based biomarkers are effective, ranging from 3.011 to 35.233 and exhibit consistent diagnostic patterns with clinic experience. Moreover, a human-AI collaborative diagnosis test is conducted and the participants with collaboration achieved a performance exceeding that of both humans and AI. By synergistically integrating interpretability and interaction, this study facilitates the convergence of clinicians' expertise and data-driven intelligence. The promotion of inexperienced ophthalmologists with the aid of AI-based biomarkers, as well as increased AI prediction by intervention from experienced ones, demonstrate a promising diagnostic paradigm for infectious keratitis using KGDM, which holds the potential for extension to other diseases where experienced medical practitioners are limited and the safety of AI is concerned.

Related papers

• Clinical Evaluation of Medical Image Synthesis: A Case Study in Wireless Capsule Endoscopy [63.39037092484374]
  This study focuses on the clinical evaluation of medical Synthetic Data Generation using Artificial Intelligence (AI) models. The paper contributes by a) presenting a protocol for the systematic evaluation of synthetic images by medical experts and b) applying it to assess TIDE-II, a novel variational autoencoder-based model for high-resolution WCE image synthesis. The results show that TIDE-II generates clinically relevant WCE images, helping to address data scarcity and enhance diagnostic tools.
  arXiv  Detail & Related papers  (2024-10-31T19:48:50Z)
• TrialBench: Multi-Modal Artificial Intelligence-Ready Clinical Trial Datasets [57.067409211231244]
  This paper presents meticulously curated AIready datasets covering multi-modal data (e.g., drug molecule, disease code, text, categorical/numerical features) and 8 crucial prediction challenges in clinical trial design. We provide basic validation methods for each task to ensure the datasets' usability and reliability. We anticipate that the availability of such open-access datasets will catalyze the development of advanced AI approaches for clinical trial design.
  arXiv  Detail & Related papers  (2024-06-30T09:13:10Z)
• Enhancing Breast Cancer Diagnosis in Mammography: Evaluation and Integration of Convolutional Neural Networks and Explainable AI [0.0]
  The study presents an integrated framework combining Convolutional Neural Networks (CNNs) and Explainable Artificial Intelligence (XAI) for the enhanced diagnosis of breast cancer. The methodology encompasses an elaborate data preprocessing pipeline and advanced data augmentation techniques to counteract dataset limitations. A focal point of our study is the evaluation of XAI's effectiveness in interpreting model predictions.
  arXiv  Detail & Related papers  (2024-04-05T05:00:21Z)
• The Limits of Perception: Analyzing Inconsistencies in Saliency Maps in XAI [0.0]
  Explainable artificial intelligence (XAI) plays an indispensable role in demystifying the decision-making processes of AI. As they operate as "black boxes," with their reasoning obscured and inaccessible, there's an increased risk of misdiagnosis. This shift towards transparency is not just beneficial -- it's a critical step towards responsible AI integration in healthcare.
  arXiv  Detail & Related papers  (2024-03-23T02:15:23Z)
• ContrastDiagnosis: Enhancing Interpretability in Lung Nodule Diagnosis Using Contrastive Learning [23.541034347602935]
  Clinicians' distrust of black box models has hindered the clinical deployment of AI products. We propose ContrastDiagnosis, a straightforward yet effective interpretable diagnosis framework. High diagnostic accuracy was achieved with AUC of 0.977 while maintain a high transparency and explainability.
  arXiv  Detail & Related papers  (2024-03-08T13:00:52Z)
• Deciphering knee osteoarthritis diagnostic features with explainable artificial intelligence: A systematic review [4.918419052486409]
  Existing artificial intelligence models for diagnosing knee osteoarthritis (OA) have faced criticism for their lack of transparency and interpretability. Recently, explainable artificial intelligence (XAI) has emerged as a specialized technique that can provide confidence in the model's prediction. This paper presents the first survey of XAI techniques used for knee OA diagnosis.
  arXiv  Detail & Related papers  (2023-08-18T08:23:47Z)
• Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges [58.32937972322058]
  "Medico automatic polyp segmentation (Medico 2020)" and "MedAI: Transparency in Medical Image (MedAI 2021)" competitions. We present a comprehensive summary and analyze each contribution, highlight the strength of the best-performing methods, and discuss the possibility of clinical translations of such methods into the clinic.
  arXiv  Detail & Related papers  (2023-07-30T16:08:45Z)
• GENIE-NF-AI: Identifying Neurofibromatosis Tumors using Liquid Neural Network (LTC) trained on AACR GENIE Datasets [0.0]
  We propose an interpretable AI approach to diagnose patients with neurofibromatosis. Our proposed approach outperformed existing models with 99.86% accuracy.
  arXiv  Detail & Related papers  (2023-04-26T10:28:59Z)
• Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes [50.8044927215346]
  We consider a comorbidity risk prediction scenario and focus on contexts regarding the patients clinical state. We employ several state-of-the-art LLMs to present contexts around risk prediction model inferences and evaluate their acceptability. Our paper is one of the first end-to-end analyses identifying the feasibility and benefits of contextual explanations in a real-world clinical use case.
  arXiv  Detail & Related papers  (2023-02-11T18:07:11Z)
• Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration [55.88616573143478]
  Outcome prediction from clinical text can prevent doctors from overlooking possible risks. Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction are four common outcome prediction targets. We propose clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources.
  arXiv  Detail & Related papers  (2021-02-08T10:26:44Z)
• Inheritance-guided Hierarchical Assignment for Clinical Automatic Diagnosis [50.15205065710629]
  Clinical diagnosis, which aims to assign diagnosis codes for a patient based on the clinical note, plays an essential role in clinical decision-making. We propose a novel framework to combine the inheritance-guided hierarchical assignment and co-occurrence graph propagation for clinical automatic diagnosis.
  arXiv  Detail & Related papers  (2021-01-27T13:16:51Z)

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.