{"doi":"10.12968/hmed.2024.0034","title":"Using Artificial Intelligence to predict outcomes of operatively managed neck of femur fractures","abstract":"<jats:p>Aims/Background Patients with neck of femur fractures present a tremendous public health problem that leads to a high incidence of death and dysfunction. An essential factor is the postoperative length of stay, which heavily impacts hospital costs and the quality of care. As an extension of traditional statistical methods, machine learning (ML) provides the possibility of accurately predicting the length of hospital stay. This review assesses how machine learning can effectively use healthcare data to predict the outcomes of patients with operatively managed neck of femurs.</jats:p>\n                  <jats:p>Methods A narrative literature review on the use of Artificial Intelligence to predict outcomes in the neck of femurs was undertaken to understand the field and critical considerations of its application. The papers and any relevant references were scrutinised using the specific inclusion and exclusion criteria to produce papers that were used in the analysis.</jats:p>\n                  <jats:p>Results Thirteen papers were used in the analysis. The critical themes recognised the different models, the 'backbox' conundrum, predictor identification, validation methodology and the need to improve efficiency and quality of care. Through reviewing the themes in this paper, current issues, and potential avenues of advancing the field are explored.</jats:p>\n                  <jats:p>Conclusions This review has demonstrated that the use of machine learning in Orthopaedic pathways is in its infancy. Further work is needed to leverage this technology effectively to improve outcomes.</jats:p>","journal":"British Journal of Hospital Medicine","year":2024,"id":663341,"datarank":0.16479184330021646,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"self_citation_contribution":0.16479184330021646,"citation_network_contribution":0.0,"self_endowment_contribution":0.16479184330021646,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1731872,"name":"Robert Ashford","orcid":null,"position":1,"is_corresponding":false},{"id":1731871,"name":"Geeth Silva","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Using Artificial Intelligence to predict outcomes of operatively managed neck of femur fractures","abstract":"<jats:p>Aims/Background Patients with neck of femur fractures present a tremendous public health problem that leads to a high incidence of death and dysfunction. An essential factor is the postoperative length of stay, which heavily impacts hospital costs and the quality of care. As an extension of traditional statistical methods, machine learning (ML) provides the possibility of accurately predicting the length of hospital stay. This review assesses how machine learning can effectively use healthcare data to predict the outcomes of patients with operatively managed neck of femurs.</jats:p>\n                  <jats:p>Methods A narrative literature review on the use of Artificial Intelligence to predict outcomes in the neck of femurs was undertaken to understand the field and critical considerations of its application. The papers and any relevant references were scrutinised using the specific inclusion and exclusion criteria to produce papers that were used in the analysis.</jats:p>\n                  <jats:p>Results Thirteen papers were used in the analysis. The critical themes recognised the different models, the 'backbox' conundrum, predictor identification, validation methodology and the need to improve efficiency and quality of care. Through reviewing the themes in this paper, current issues, and potential avenues of advancing the field are explored.</jats:p>\n                  <jats:p>Conclusions This review has demonstrated that the use of machine learning in Orthopaedic pathways is in its infancy. Further work is needed to leverage this technology effectively to improve outcomes.</jats:p>","is_dataset_classified":null,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"38941973","pmcid":null,"openalex_id":"https://openalex.org/W4399866237","authors":[],"funders":[],"total_grants":0,"fwci":0.8562,"citation_percentile":0.72712114,"influential_citations":0,"citation_trend":[{"year":2025,"count":1},{"year":2026,"count":1}],"oa_status":"hybrid","license":"cc-by-nc","oa_locations":[{"url":"https://www.magonlinelibrary.com/doi/pdf/10.12968/hmed.2024.0034?download=true","host_type":"journal"},{"url":"https://www.magonlinelibrary.com/doi/pdf/10.12968/hmed.2024.0034?download=true","host_type":"publisher"},{"url":"http://www.magonlinelibrary.com/doi/pdf/10.12968/hmed.2024.0034","host_type":"publisher"},{"url":"https://doi.org/10.12968/hmed.2024.0034","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/38941973","host_type":"repository"}],"fields_of_study":["Hip and Femur Fractures","Cardiac, Anesthesia and Surgical Outcomes","Bone fractures and treatments","Humans","Femoral Neck Fractures","Artificial Intelligence","Length of Stay","Machine Learning"],"mesh_terms":["Machine Learning","Artificial Intelligence","Femoral Neck Fractures","Humans","Length of Stay"],"keywords":["Medicine","Artificial intelligence","Health care","Machine learning","Identification (biology)","Inclusion and exclusion criteria","Alternative medicine","Computer science","Literature review","Orthopaedics","Neck Of Femur Fractures"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Reduced inequalities"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-12T21:19:58.831006Z","pmid":null,"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":[]}