<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Haq IU</submitter><funding>Zhe Liu</funding><pagination>477</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC12108733</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>12(5)</volume><pubmed_abstract>The majority of data collected and obtained from various sources over a patient's lifetime can be assumed to comprise pertinent information for delivering the best possible treatment. Medical data, such as radiographic and histopathology images, electrocardiograms, and medical records, all guide a physician's diagnostic approach. Nevertheless, most machine learning techniques in the healthcare field emphasize data analysis from a single modality, which is insufficiently reliable. This is especially evident in radiology, which has long been an essential topic of machine learning in healthcare because of its high data density, availability, and interpretation capability. In the future, computer-assisted diagnostic systems must be intelligent to process a variety of data simultaneously, simil</pubmed_abstract><journal>Bioengineering (Basel, Switzerland)</journal><pubmed_title>Advancements in Medical Radiology Through Multimodal Machine Learning: A Comprehensive Overview.</pubmed_title><pmcid>PMC12108733</pmcid><funding_grant_id>62276116</funding_grant_id><pubmed_authors>Liu Z</pubmed_authors><pubmed_authors>Haq IU</pubmed_authors><pubmed_authors>Osman H</pubmed_authors><pubmed_authors>Mhamed M</pubmed_authors><pubmed_authors>Hamd ZY</pubmed_authors><pubmed_authors>Al-Harbi M</pubmed_authors></additional><is_claimable>false</is_claimable><name>Advancements in Medical Radiology Through Multimodal Machine Learning: A Comprehensive Overview.</name><description>The majority of data collected and obtained from various sources over a patient's lifetime can be assumed to comprise pertinent information for delivering the best possible treatment. Medical data, such as radiographic and histopathology images, electrocardiograms, and medical records, all guide a physician's diagnostic approach. Nevertheless, most machine learning techniques in the healthcare field emphasize data analysis from a single modality, which is insufficiently reliable. This is especially evident in radiology, which has long been an essential topic of machine learning in healthcare because of its high data density, availability, and interpretation capability. In the future, computer-assisted diagnostic systems must be intelligent to process a variety of data simultaneously, simil</description><dates><release>2025-01-01T00:00:00Z</release><publication>2025 Apr</publication><modification>2025-07-26T03:07:02.681Z</modification><creation>2025-07-26T03:07:02.681Z</creation></dates><accession>S-EPMC12108733</accession><cross_references><pubmed>40428096</pubmed><doi>10.3390/bioengineering12050477</doi></cross_references></HashMap>