Logo Lanfrica
  • Home
  • Atlas
  • Insights
  • Docs
  • Sign in

© 2026 Lanfrica. All rights reserved. All copyrights of the resources shown on the Lanfrica website belong to the original copyright holders, unless explicitly stated otherwise.

Lessons from shortcomings in machine learning for medical imaging

Domain:

healthcare

Record type:

paper
Creator:
VarChe
Editor:
MétIT OECD
Publisher:
CCSD
Host:avatar
Machine learning for medical imaging data has many opportunities for improving patients’ health, and has attracted a lot of attention in recent years. However, the progress of the field as a whole is being slowed down by the current incentives in (machine learning) research. In this report we summarize our findings based onliterature and our own analysis, namely that larger datasets and more deep learning algorithms do not yet provide practical improvements in clinical problems. We provide recommendations for practices to adopt within research communities, as well as what we believe needs to change within research policy, to increase the impact of artificial intelligence in this field.

Visit

hal.science

Tags

[INFO]Computer Science [cs]

Licenses

info:eu-repo/semantics/OpenAccess

Similar

Quantized Machine Learning Models for Medical Imaging in Low-Resource Healthcare SettingsDeep learning for medical imaging in developing nationsDeep Learning in Medical Imaging: Using Densenet121 for Automated Tuberculosis Detection from Chest X-RaysInfrastructure Readiness Assessment Questionnaire for Federated Learning in Medical Imaging in AfricaA Study of Acquisition Functions for Medical Imaging Deep Active LearningEXPLORING MACHINE LEARNING POTENTIALS TO IMPROVE MEDICAL IMAGING SERVICES OF CHILDREN AND ADOLESCENTS IN LOW-RESOURCE SETTINGS

Quantized Machine Learning Models for Medical Imaging in Low-Resource Healthcare Settings

Deep learning models have shown strong performance in medical image analysis, but deploying them in

Deep learning for medical imaging in developing nations

Deep learning research and innovation have primarily been focused on high- income countries with abu

Deep Learning in Medical Imaging: Using Densenet121 for Automated Tuberculosis Detection from Chest X-Rays

ABSTRACT:Millions of reported cases and associated deaths highlight the annual global threa

Infrastructure Readiness Assessment Questionnaire for Federated Learning in Medical Imaging in Africa

This document is a questionaaire used  in AFRICAI-RI project. The purpose of this questionnaire is t

A Study of Acquisition Functions for Medical Imaging Deep Active Learning

A Study of Acquisition Functions for Medical Imaging Deep Active Learning

Poster presented at the Deep Learning Indaba 2023 by Bonaventure F. P. Dossou

EXPLORING MACHINE LEARNING POTENTIALS TO IMPROVE MEDICAL IMAGING SERVICES OF CHILDREN AND ADOLESCENTS IN LOW-RESOURCE SETTINGS

The existing body of evidence in literature raises concerns over the growing overuse, underuse, and