Foundation Models in Medical Image Analysis: Overview and Prospects

Foundation models have emerged as remarkable tools in medical image analysis leading to a new era of smart healthcare and offering sophisticated capabilities to enhance diagnostic accuracy, treatment planning, and patient outcomes. This paper provides a comprehensive overview of the current standing and future prospects of foundation models in the field of medical imaging. We briefly highlight some applications across diverse imaging modalities such as MRI, CT, and ultrasound and discuss limitations and challenges. Future prospective and research directions are discussed to guide future exploration.

Authors

Institutions

Publication Details

Published
2024-11-20
DOI
https://doi.org/10.1109/fmlds63805.2024.00011
Citations
1
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.40

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Foundation Models in Medical Image Analysis: Overview and Prospects

Essam A. Rashed, Mahmoud Bekhit
1 citations
Radiomics and Machine Learning in Medical Imaging
0.40
article

Foundation Models in Medical Image Analysis: Overview and Prospects

Essam A. Rashed, Mahmoud Bekhit
article en
1 citations

Abstract

Foundation models have emerged as remarkable tools in medical image analysis leading to a new era of smart healthcare and offering sophisticated capabilities to enhance diagnostic accuracy, treatment planning, and patient outcomes. This paper provides a comprehensive overview of the current standing and future prospects of foundation models in the field of medical imaging. We briefly highlight some applications across diverse imaging modalities such as MRI, CT, and ultrasound and discuss limitations and challenges. Future prospective and research directions are discussed to guide future exploration.

University of Hyogo (JP), Australian Catholic University (AU)
Japan Science and Technology Corporation, Precursory Research for Embryonic Science and Technology
Gender equality
Openalex Percentile: Top 31%
Radiomics and Machine Learning in Medical Imaging
0.40
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Foundation Models in Medical Image Analysis: Overview and Prospects — Essam A. Rashed, Mahmoud Bekhit · (2024) | TGRS Research Map | TGRS