<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>23(8)</volume><submitter>Bittrich M</submitter><funding>Bayerisch–Tschechische Hochschulagentur</funding><funding>Wilhelm Sander-Stiftung</funding><funding>Deutsche Krebshilfe</funding><funding>Julius-Maximilians-Universität Würzburg</funding><funding>Universitätsklinikum Würzburg</funding><funding>Else Kröner-Fresenius-Stiftung</funding><funding>Interdisziplinäres Zentrum für Klinische Forschung, Universitätsklinikum Würzburg</funding><pubmed_abstract>In addition to randomized clinical trials, consideration of Real-World Evidence is necessary for mirroring clinical reality. However, processing such evidence for large numbers of patients often requires considerable time and effort. This is particularly true for rare tumor diseases such as multiple myeloma (MM) or for adverse effects that occur even more rarely. In such cases, artificial intelligence is able to efficiently detect patients with rare conditions. One of these rare adverse events, and the most discussed, following bone protective treatment in MM is medication-related osteonecrosis of the jaw (MRONJ). The association of bone protective treatment to MM outcome has been intensively studied. However, the impact of MRONJ resulting from such treatment on MM prognosis and outcome is</pubmed_abstract><journal>Clinical and experimental medicine</journal><pagination>5215-5226</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC10725344</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>Does medication-related osteonecrosis of the jaw affect survival of patients with Multiple Myeloma?: Exploring a large single center database using artificial intelligence.</pubmed_title><pmcid>PMC10725344</pmcid><pubmed_authors>Beilhack A</pubmed_authors><pubmed_authors>Kerscher A</pubmed_authors><pubmed_authors>Einsele H</pubmed_authors><pubmed_authors>Krebs M</pubmed_authors><pubmed_authors>Loda S</pubmed_authors><pubmed_authors>Bittrich M</pubmed_authors><pubmed_authors>Knop S</pubmed_authors><pubmed_authors>Zhou X</pubmed_authors><pubmed_authors>Kortum KM</pubmed_authors><pubmed_authors>Solimando AG</pubmed_authors><pubmed_authors>Danhof S</pubmed_authors><pubmed_authors>Rasche L</pubmed_authors><pubmed_authors>Anton S</pubmed_authors><pubmed_authors>Hartmann S</pubmed_authors><pubmed_authors>Hetterich R</pubmed_authors></additional><is_claimable>false</is_claimable><name>Does medication-related osteonecrosis of the jaw affect survival of patients with Multiple Myeloma?: Exploring a large single center database using artificial intelligence.</name><description>In addition to randomized clinical trials, consideration of Real-World Evidence is necessary for mirroring clinical reality. However, processing such evidence for large numbers of patients often requires considerable time and effort. This is particularly true for rare tumor diseases such as multiple myeloma (MM) or for adverse effects that occur even more rarely. In such cases, artificial intelligence is able to efficiently detect patients with rare conditions. One of these rare adverse events, and the most discussed, following bone protective treatment in MM is medication-related osteonecrosis of the jaw (MRONJ). The association of bone protective treatment to MM outcome has been intensively studied. However, the impact of MRONJ resulting from such treatment on MM prognosis and outcome is</description><dates><release>2023-01-01T00:00:00Z</release><publication>2023 Dec</publication><modification>2026-06-03T12:00:10.118Z</modification><creation>2025-04-04T12:52:07.333Z</creation></dates><accession>S-EPMC10725344</accession><cross_references><pubmed>37805620</pubmed><doi>10.1007/s10238-023-01100-6</doi></cross_references></HashMap>