Artificial Intelligence, Medical Imaging, and Precision Medicine: Advances and Perspectives

The role of artificial intelligence (AI) applied to medical imaging in developing and strengthening personalized medicine is described as a continuous process of improvement, a set of opportunities, and a professional challenge of enormous significance. This paper outlines the main processes in which AI is involved regarding imaging, including data preparation, image harmonization, automatic segmentation of organs and lesions, labeling, extraction of radiomic features, and the development of predictive clinical models. It will also address aspects related to the integration of these solutions into clinical practice to enhance accuracy and efficiency in the care process, diagnosis, and treatment, making it more personalized, efficient, and precise. Projects like PRIMAGE and CHAIMELEON highlight the transformative potential of AI and the fundamental role of interdisciplinary collaboration in realizing this potential, based on ongoing multiprofesional collaboration to address the ethical, regulatory, technical, and clinical challenges that accompany these advancements.

Authors

Institutions

Publication Details

Journal
ANALES RANM
Published
2024-08-31
DOI
https://doi.org/10.32440/ar.2024.141.02.rev02
Citations
2
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.81
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Artificial Intelligence, Medical Imaging, and Precision Medicine: Advances and Perspectives

Luis Martí‐Bonmatí
2 citations
ANALES RANM
Radiomics and Machine Learning in Medical Imaging
0.81
article

Artificial Intelligence, Medical Imaging, and Precision Medicine: Advances and Perspectives

Luis Martí‐Bonmatí
article en
2 citations

Abstract

The role of artificial intelligence (AI) applied to medical imaging in developing and strengthening personalized medicine is described as a continuous process of improvement, a set of opportunities, and a professional challenge of enormous significance. This paper outlines the main processes in which AI is involved regarding imaging, including data preparation, image harmonization, automatic segmentation of organs and lesions, labeling, extraction of radiomic features, and the development of predictive clinical models. It will also address aspects related to the integration of these solutions into clinical practice to enhance accuracy and efficiency in the care process, diagnosis, and treatment, making it more personalized, efficient, and precise. Projects like PRIMAGE and CHAIMELEON highlight the transformative potential of AI and the fundamental role of interdisciplinary collaboration in realizing this potential, based on ongoing multiprofesional collaboration to address the ethical, regulatory, technical, and clinical challenges that accompany these advancements.

ANALES RANMVol. 141(141(02))
Real Academia Española (ES)
Partnerships for the goals
Openalex Percentile: Top 26%
Radiomics and Machine Learning in Medical Imaging
0.81
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.

Artificial Intelligence, Medical Imaging, and Precision Medicine: Advances and Perspectives — Luis Martí‐Bonmatí · ANALES RANM (2024) | TGRS Research Map | TGRS