Designing Interpretable ML System to Enhance Trust in Healthcare: A Systematic Review to Proposed Responsible Clinician-AI-Collaboration Framework

18 November 2023

Papers citing "Designing Interpretable ML System to Enhance Trust in Healthcare: A Systematic Review to Proposed Responsible Clinician-AI-Collaboration Framework"

5 / 5 papers shown

Title
Interpretable Multimodal Learning for Tumor Protein-Metal Binding: Progress, Challenges, and Perspectives Xiaokun Liu Sayedmohammadreza Rastegari Yijun Huang Sxe Chang Cheong Weikang Liu ... Sina Tabakhi Xianyuan Liu Zheqing Zhu Wei Sang Haiping Lu 29 0 0 04 Apr 2025
A New Approach for Interpretability and Reliability in Clinical Risk Prediction: Acute Coronary Syndrome Scenario Francisco Valente J. Henriques Simão Paredes Teresa Rocha Paulo de Carvalho João Morais OOD 14 22 0 15 Oct 2021
EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: the MonuMAI cultural heritage use case Natalia Díaz Rodríguez Alberto Lamas Jules Sanchez Gianni Franchi Ivan Donadello S. Tabik David Filliat P. Cruz Rosana Montes Francisco Herrera 49 77 0 24 Apr 2021
Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyond Guang Yang Qinghao Ye Jun Xia 92 480 0 03 Feb 2021
SMOTE: Synthetic Minority Over-sampling Technique Nitesh V. Chawla Kevin W. Bowyer Lawrence Hall W. Kegelmeyer AI4TS 163 25,256 0 09 Jun 2011