{"doi":"10.3233/xst-240051","title":"Can AI generate diagnostic reports for radiologist approval on CXR images? A multi-reader and multi-case observer performance study","abstract":"BACKGROUND: Accurately detecting a variety of lung abnormalities from heterogenous chest X-ray (CXR) images and writing radiology reports is often difficult and time-consuming. OBJECTIVE: To access the utility of a novel artificial intelligence (AI) system (MOM-ClaSeg) in enhancing the accuracy and efficiency of radiologists in detecting heterogenous lung abnormalities through a multi-reader and multi-case (MRMC) observer performance study. METHODS: Over 36,000 CXR images were retrospectively collected from 12 hospitals over 4 months and used as the experiment group and the control group. In the control group, a double reading method is used in which two radiologists interpret CXR to generate a final report, while in the experiment group, one radiologist generates the final reports based on AI-generated reports. RESULTS: Compared with double reading, the diagnostic accuracy and sensitivity of single reading with AI increases significantly by 1.49% and 10.95%, respectively (P < 0.001), while the difference in specificity is small (0.22%) and without statistical significance (P = 0.255). Additionally, the average image reading and diagnostic time in the experimental group is reduced by 54.70% (P < 0.001). CONCLUSION: This MRMC study demonstrates that MOM-ClaSeg can potentially serve as the first reader to generate the initial diagnostic reports, with a radiologist only reviewing and making minor modifications (if needed) to arrive at the final decision. It also shows that single reading with AI can achieve a higher diagnostic accuracy and efficiency than double reading.","journal":"Journal of X-Ray Science and Technology","year":2024,"id":504889,"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":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9552,"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":1346263,"name":"Xia Li","orcid":"0000-0003-1328-9061","position":1,"is_corresponding":false},{"id":315915,"name":"Qiuting Zheng","orcid":null,"position":2,"is_corresponding":false},{"id":692179,"name":"Bin Zheng","orcid":"0000-0002-7682-6648","position":3,"is_corresponding":false},{"id":75948,"name":"Stefan Jaeger","orcid":"0000-0001-6877-4318","position":4,"is_corresponding":false},{"id":250875,"name":"Maryellen L. Giger","orcid":"0000-0001-5482-9728","position":5,"is_corresponding":false},{"id":450235,"name":"Jordan Fuhrman","orcid":null,"position":6,"is_corresponding":false},{"id":1355743,"name":"Hui Li","orcid":"0000-0002-5401-3013","position":7,"is_corresponding":false},{"id":314146,"name":"Fleming Lure","orcid":"0000-0001-5655-6831","position":9,"is_corresponding":false},{"id":76208,"name":"Hongjun Li","orcid":"0000-0002-1765-8445","position":10,"is_corresponding":false},{"id":1355744,"name":"Li Li","orcid":"0000-0001-5585-743X","position":12,"is_corresponding":false},{"id":483445,"name":"Lin Guo","orcid":"0000-0002-5910-3464","position":0,"is_corresponding":true}],"reference_count":21,"raw_metadata":null,"created_at":"2026-07-19T02:10:43.302033Z","pmid":"39422982","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":[]}