{"doi":"10.1002/acm2.70226","title":"Current progress of digital twin construction using medical imaging","abstract":"Medical imaging is fundamental to digital twin technology, enabling patient-specific virtual models of anatomy and physiology. By integrating high-resolution modalities (Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), ultrasound) with computational frameworks, recent imaging advances now support real-time simulation, predictive modeling, and earlier disease detection. Such capabilities directly inform individualized treatment planning and contribute to more precise, personalized care. Despite remaining challenges-complex anatomical modeling, multimodal integration, and high computational demands-recent advances in imaging and machine learning have significantly enhanced the accuracy and clinical utility of digital twins. The main contributions of our review are: (1) a system-by-system classification of methodologies; (2) evidence that advanced imaging modalities have improved diagnostic accuracy, treatment effectiveness, and patient outcomes beyond conventional approaches; and (3) identification of remaining technical bottlenecks. We further analyze key technical barriers-such as data scarcity and computational complexity-and outline future directions (e.g., AI-driven data augmentation, real-time model optimization) to unlock digital twins' full potential in precision medicine.","journal":"Journal of Applied Clinical Medical Physics","year":2025,"id":509855,"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":26,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9486,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1349957,"name":"Yizhou Wu","orcid":"0009-0003-6363-4561","position":1,"is_corresponding":false},{"id":872494,"name":"Mingzhe Hu","orcid":"0000-0001-9808-4967","position":2,"is_corresponding":false},{"id":849942,"name":"Chih‐Wei Chang","orcid":"0000-0002-3818-4381","position":3,"is_corresponding":false},{"id":374408,"name":"Ruirui Liu","orcid":"0000-0002-5314-8477","position":4,"is_corresponding":false},{"id":1017022,"name":"Richard L. J. Qiu","orcid":"0000-0002-7877-1900","position":5,"is_corresponding":false},{"id":236226,"name":"Xiaofeng Yang","orcid":"0000-0001-9023-5855","position":6,"is_corresponding":false},{"id":1365055,"name":"Feng Zhao","orcid":"0009-0004-0412-9108","position":0,"is_corresponding":true}],"reference_count":112,"raw_metadata":null,"created_at":"2026-07-19T02:47:30.942539Z","pmid":"40841176","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":[]}