{"doi":"10.1007/s00134-024-07491-8","title":"Why federated learning will do little to overcome the deeply embedded biases in clinical medicine","abstract":"We read with great interest the article by van Genderen et al. [1] which provides a contemporary and comprehensive overview of the potential of federating data access and data sharing in intensive care.Importantly, the authors list the perpetuation of biases encoded in clinical care practice as a major potential shortcoming.Furthermore, they state that this could be mitigated by \"ensuring an adequate representation of hospitals from various regions worldwide could lead to more diverse and inclusive health datasets.\"We agree that the use of diverse and inclusive health datasets should be promoted as a necessary first step to build fair machine learning algorithms.However, we do not believe that this will be sufficient to overcome the deeply embedded biases in medicine from a knowledge system that is designed around a majoritized few.Even with high quality data from the intensive care units from across the world, the social patterning of the data generation process can still produce artificial intelligence (AI) that is bound to preserve and even scale existing disparities in care with resulting inequities in patient outcomes.There are numerous examples of data issues that stem from the social patterning of the data capture and data generation process (Fig. 1).These include, but are certainly not limited to, (1) the differential performance of medical devices used to measure physiologic signals","journal":"Intensive Care Medicine","year":2024,"id":455960,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":6,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9617,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1280045,"name":"Gernot Pucher","orcid":"0000-0002-3519-0217","position":1,"is_corresponding":false},{"id":4662,"name":"Leo Anthony Celi","orcid":"0000-0001-6712-6626","position":2,"is_corresponding":false},{"id":668230,"name":"Christopher Martin Sauer","orcid":"0000-0002-2388-5919","position":0,"is_corresponding":true}],"reference_count":4,"raw_metadata":null,"created_at":"2026-07-19T02:03:22.974789Z","pmid":"38829532","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}