Intelligent medicine最新文献

{"title":"Deep learning with data transformation improves cancer risk prediction in oral precancerous conditions","authors":"John Adeoye,&nbsp;Yuxiong Su","doi":"10.1016/j.imed.2024.11.003","DOIUrl":"10.1016/j.imed.2024.11.003","url":null,"abstract":"<div><h3>Background</h3><div>Oral cancer is the most common head and neck malignancy and may develop from oral leukoplakia (OL) and oral lichenoid disease (OLD). Machine learning classifiers using structured (tabular) data have been employed to predict malignant transformation in OL and OLD. However, current models require improved discrimination, and their frameworks may limit feature fusion and multimodal risk prediction. Therefore, this study investigates whether tabular-to-image data conversion and deep learning (DL) based on convolutional neural networks (CNNs) can improve malignant transformation prediction compared to traditional classifiers.</div></div><div><h3>Methods</h3><div>This study used retrospective data of 1,010 patients with OL and OLD treated at Queen Mary Hospital, Hong Kong, from January 2003 to December 2023, to construct artificial intelligence-based models for oral cancer risk stratification in OL/OLD. Twenty-five input features and information on oral cancer development in OL/OLD were retrieved from electronic health records. Tabular-to-2D image data transformation was achieved by creating a feature matrix from encoded labels of the input variables arranged according to their correlation coefficient. Then, 2D images were used to populate five pre-trained DL models (VGG16, VGG19, MobileNetV2, ResNet50, and EfficientNet-B0). Area under the receiver operating characteristic curve (AUC), Brier scores, and net benefit of the DL models were calculated and compared to five traditional classifiers based on structured data and the binary epithelial dysplasia grading system (current method).</div></div><div><h3>Results</h3><div>This study found that the DL models had better AUC values (0.893–0.955) and Brier scores (0.072–0.106) compared to the traditional classifiers (AUC: 0.887–0.941 and Brier score: 0.074–0.136) during validation. During internal testing, VGG16 and VGG19 had better AUC values and Brier scores than other CNNs (AUC: 0.998–1.00; Brier score: 0.036–0.044) and the best traditional classifier (random forest) (AUC: 0.906; Brier score: 0.153). Additionally, VGG16 and VGG19 models outperformed random forest in discrimination and calibration during external testing (AUC: 1.00 <em>vs</em>. 0.976; Brier score: 0.022–0.034 <em>vs</em>. 0.129). The best CNNs also had better discriminatory performance and calibration than binary dysplasia grading at internal and external testing. Overall, decision curve analysis showed that the optimal DL models with transformed data had a higher net benefit than random forest and binary dysplasia grading.</div></div><div><h3>Conclusion</h3><div>Tabular-to-2D image data transformation may improve the use of structured input features for developing optimal intelligent models for oral cancer risk prediction in OL and OLD using convolutional networks. This approach may have the potential to robustly handle structured data in multimodal DL frameworks for oncological outcome prediction.</div></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"5 2","pages":"Pages 141-150"},"PeriodicalIF":4.4,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence-powered precision: Unveiling the tumor microenvironment for a new frontier in personalized cancer therapy","authors":"Songwei Feng ,&nbsp;Xia Yin ,&nbsp;Yang Shen","doi":"10.1016/j.imed.2025.02.001","DOIUrl":"10.1016/j.imed.2025.02.001","url":null,"abstract":"<div><div>The tumor microenvironment (TME) is a pivotal determinant of cancer progression and therapeutic response. The advent of individualized tumor therapy, based on the in-depth analysis of the TME, represents a revolutionary transformation in oncology. Artificial intelligence (AI) provides unparalleled capabilities to analyze and decipher the complexities of the TME through multi-omics integration, spatial modeling, and predictive analytics. By combining computational innovations with clinical insights, AI is driving a new paradigm in precision medicine. This editorial explores the transformative potential of AI in individualized tumor therapy, highlighting the groundbreaking applications and strategic directions to advance this field.</div></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"5 2","pages":"Pages 95-98"},"PeriodicalIF":4.4,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Osteosarcoma knowledge graph question answering system: deep learning-based knowledge graph and large language model fusion","authors":"Lulu Zhang ,&nbsp;Weisong Zhao ,&nbsp;Zhiwei Cheng ,&nbsp;Yafei Jiang ,&nbsp;Kai Tian ,&nbsp;Jia Shi ,&nbsp;Zhenyu Jiang ,&nbsp;Yingqi Hua","doi":"10.1016/j.imed.2024.12.001","DOIUrl":"10.1016/j.imed.2024.12.001","url":null,"abstract":"<div><h3>Objective</h3><div>Osteosarcoma is a prevalent primary malignant bone tumor in children and adolescents, accounting for approximately 5 % of childhood malignancies. Because of its rarity and biological complexity, treatment breakthroughs for osteosarcoma have been limited. To advance research in this field, we aimed to construct the first comprehensive osteosarcoma knowledge graph (OSKG) using the PubMed database.</div></div><div><h3>Methods</h3><div>A systematic search of PubMed (2003–2023) using the keyword “osteosarcoma” yielded 25,415 abstracts. Leveraging BioBERT, pretrained on biomedical corpora and fine-tuned with osteosarcoma-specific manual annotations, we identified 16 entity types and 17 biological relationships. The extracted elements were synthesized to create the OSKG, resulting in a deep learning-based knowledge base to explore osteosarcoma pathogenesis and molecular mechanisms. We then developed a specialized question-answering system (knowledge graph question answering (KGQA)) powered by ChatGLM3. This system employs advanced natural language processing and incorporates the OSKG to ensure optimal response quality and accuracy.</div></div><div><h3>Results</h3><div>The pretrained BioBERT averaged &gt; 92 % accuracy in entity and relationship training. Evaluation using 100 pairs of gold-standard quizzes showed that the final quiz system outperformed other large language models in accuracy and robustness.</div></div><div><h3>Conclusion</h3><div>The system is designed to provide accurate disease-related queries and answers, effectively facilitating knowledge acquisition and reasoning in medical research and clinical practice. This project offers a robust tool for osteosarcoma research and promotes the deep integration of knowledge graphs and artificial intelligence technologies in the medical field.</div></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"5 2","pages":"Pages 99-110"},"PeriodicalIF":4.4,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational interrogation of natural compounds identified resveratrol-3-O-D-glucopyranoside as a potential inhibitor of essential monkeypox virus proteins","authors":"Oluwafemi A. Adepoju ,&nbsp;Ammar Usman Danazumi ,&nbsp;Lamin BS Dibba ,&nbsp;Bashiru Ibrahim ,&nbsp;Salahuddin Iliyasu Gital ,&nbsp;Joseph Gideon Ibrahim ,&nbsp;Maliyogbinda L. Jibrailu ,&nbsp;Emmanuel O. Balogun","doi":"10.1016/j.imed.2024.09.007","DOIUrl":"10.1016/j.imed.2024.09.007","url":null,"abstract":"<div><h3>Background</h3><div>Monkeypox has become a significant public health concern owing to the recent epidemics and associated morbidity. The treatment is limited by the availability of drugs, especially in endemic communities. Computational methods can facilitate the discovery and development of new and effective therapies that are affordable. This study was aimed at identifying potential drug candidates from the SuperNatural chemical library against monkeypox virus essential proteins using computational methods.</div></div><div><h3>Methods</h3><div>We identified 7 highly conserved essential proteins involved in monkeypox virus (MPXV) replication, infectivity, and propagation as potential therapeutic targets. A library of 447 orally administrable drug-like compounds from the SuperNatural database was screened against the proteins for potential binders/ligands associations using virtual screening and molecular dynamics simulations.</div></div><div><h3>Results</h3><div>Our search identified hit compounds that mimicked the tecovirimat binding pose and outperformed it in binding affinity. Notably, resveratrol-3-O-D-glucopyranoside showed significant binding affinity to the viral protein F13L, a key protein involved in MPXV transmission. Extensive molecular dynamics simulations showed stable interactions between resveratrol-3-O-β-D-glucopyranoside and F13L, and other hit compounds with their respective targets.</div></div><div><h3>Conclusion</h3><div>Although the predicted interactions require further experimental validation, our results suggested that the identified compounds could be promising therapeutic candidates for the development of novel monkeypox drugs. These findings might underscore the significance of natural compounds in drug discovery and lay the foundation for developing novel antivirals against monkeypox.</div></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"5 1","pages":"Pages 5-13"},"PeriodicalIF":4.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nationwide survey of the status of artificial intelligence-based intracranial aneurysm detection systems","authors":"Xinran Wang ,&nbsp;Zhao Shi ,&nbsp;Xiaoqian Ji ,&nbsp;Bin Hu ,&nbsp;Sui Chen ,&nbsp;Longjiang Zhang","doi":"10.1016/j.imed.2024.11.001","DOIUrl":"10.1016/j.imed.2024.11.001","url":null,"abstract":"<div><h3>Objective</h3><div>Intracranial aneurysm imaging artificial intelligence (AI) products have entered the clinical implementation phase, but the application status of them in Chinese hospitals remains unclear. A nationwide survey was conducted to explore the current status of intracranial aneurysm imaging AI products in hospitals across China.</div></div><div><h3>Methods</h3><div>Delphi method was used to develop a questionnaire, which was then distributed to the radiologists across China between September 3rd and 10th, 2023. Independent predictors of the adoption of these AI products, radiologists' attitudes, concerns and knowledge about these AI products were evaluated using logistic regression. Participants were categorized into seven groups based on Chinese geographical regions to compare the performance of these AI products in different geographical regions.</div></div><div><h3>Results</h3><div>After 3 rounds of Delphi discussion by 29 radiologists, the questionnaire was derived. A total of 961 radiologists from 777 different hospitals in 31 provinces across China completed the questionnaire. Among these hospitals, 45.4% (353/777) had introduced intracranial aneurysm imaging AI products. The most commonly reported concern with these AI products was poor specificity (265/446, 59.4%). The majority of respondents had basic (310/961, 42.0%) or intermediate (331/961, 44.9%) knowledge of AI products and they held positive attitudes (913/961, 95.0%) towards using them. Those who had received AI training were more likely to possess a higher level of knowledge about AI (odds ratio (OR) = 1.80, <em>P</em> = 0.04). For regional comparison, respondents in Central China and East China gave the highest ratings to the accuracy (OR = 2.41, <em>P</em> = 0.048 <em>vs</em>. OR=2.36, <em>P</em> = 0.02) and specificity (OR = 2.34, <em>P</em> = 0.046 <em>vs.</em> OR = 2.37, <em>P</em> = 0.02) of these AI products.</div></div><div><h3>Conclusion</h3><div>The intracranial aneurysm imaging AI products may be widely used in Chinese hospitals but vary by clinical scenarios and geographic position.</div></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"5 1","pages":"Pages 37-45"},"PeriodicalIF":4.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"2024 Chinese guideline on the construction and application of medical blockchain#","authors":"Xiaoping Chen ,&nbsp;Feng Cao ,&nbsp;Qing Wang ,&nbsp;Zhewei Ye","doi":"10.1016/j.imed.2024.09.002","DOIUrl":"10.1016/j.imed.2024.09.002","url":null,"abstract":"<div><div>With the rapid advancement of digitalization and intelligence in the medical field, a plethora of cutting-edge technologies are gradually being applied to revolutionize healthcare. In the medical data security privacy protection and artificial intelligence encryption computing, blockchain stands out due to its inherent characteristics of traceability, tamper-proofing, and high credibility. Although blockchain technology has been applied in various industries, its application in the medical field needs more driving force, and its development needs to be standardized. This clinical practice guideline is developed following the World Health Organization's recommended process, adopting Grading of Recommendations Assessment, Development and Evaluation in assessing evidence quality. Considering the integration of blockchain and medical scenarios, we focus on the value and implementability of practical medical applications and provide the guidance on the construction and application of medical blockchain. This practice guideline includesd 10 potential medical application scenarios and usage frameworks. It is worth highlighting that electronic medical record sharing, drug and device anti-counterfeiting, medical digital intellectual property protection, and public health management are considered to be the most easily implemented and effective medical scenarios. The recommendations in this guideline were formulated based on the consideration of stakeholder values and preferences, resource utilization, feasibility, and acceptability, may have a profound impact on the construction of medical blockchain-related scenarios in China and internationally.</div><div><strong>Registration:</strong> Practice Guidance Registration for Transparency (PREPARE) website (<span><span>http://www.guidelines-registry.cn</span><svg><path></path></svg></span>) Registration No. PREPARE-2023CN637.</div></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"5 1","pages":"Pages 73-83"},"PeriodicalIF":4.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-based identification and localization of intracranial hemorrhage in patients using a large annotated head computed tomography dataset: A retrospective multicenter study","authors":"Jingjing Liu ,&nbsp;Weijie Fan ,&nbsp;Yi Yang ,&nbsp;Qi Peng ,&nbsp;Bingjun Ji ,&nbsp;Luxing He ,&nbsp;Yang Li ,&nbsp;Jing Yuan ,&nbsp;Wei Li ,&nbsp;Xianqi Wang ,&nbsp;Yi Wu ,&nbsp;Chen Liu ,&nbsp;Qingfang Gong ,&nbsp;Mi He ,&nbsp;Yeqin Fu ,&nbsp;Dong Zhang ,&nbsp;Si Zhang ,&nbsp;Yongjian Nian","doi":"10.1016/j.imed.2024.11.002","DOIUrl":"10.1016/j.imed.2024.11.002","url":null,"abstract":"<div><h3>Background</h3><div>Accurately identifying and localizing the five subtypes of intracranial hemorrhage (ICH) are crucial steps for subsequent clinical treatment; however, the lack of a large computed tomography (CT) dataset with annotations of the categorization and localization of ICH considerably limits the development of deep learning-based identification and localization methods. We aimed to construct this large dataset and develop a deep learning-based model to identify and localize the five ICH subtypes, including intraventricular hemorrhage (IVH), intraparenchymal hemorrhage (IPH), subdural hemorrhage (SDH), subarachnoid hemorrhage (SAH), and epidural hemorrhage (EDH), in non-contrast head CT scans.</div></div><div><h3>Methods</h3><div>Based on the public Radiological Society of North America (RSNA) 2019 dataset, we constructed a large CT dataset named RSNA 2019+ that was annotated for bleeding localization of the five ICH subtypes by three radiologists. An improved YOLOv8 architecture with the bidirectional feature pyramid network was proposed and trained using the RSNA 2019+ training dataset and evaluated on the RSNA 2019+ test dataset. The public CQ500, and two private datasets collected from the Xinqiao and Sunshine Union Hospitals, respectively, were also annotated to perform multicenter validation. Furthermore, the performance of the deep learning model was compared with that of four radiologists. Multiple performance metrics, including the average precision (AP), precision, recall and F1-score, were used for performance evaluation. The McNemar and chi-squared tests were performed, and the 95% Wilson confidence intervals were given for the precision and recall.</div></div><div><h3>Results</h3><div>There were 175,125; 4,707; 8,259; and 3,104 bounding boxes after annotation on the RSNA 2019+; CQ500+; and the PD 1 and PD 2 datasets, respectively. With an intersection-over-union threshold of 0.5, the APs of IVH, IPH, SAH, SDH and EDH are 0.852, 0.820, 0.574, 0.639, and 0.558, respectively, yielding a mean average precision (mAP) of 0.688 for our proposed deep learning model on the RSNA 2019+ test dataset. For the multicenter validation involving the three external datasets, the mAPs for CQ500, PD1, and PD2 were 0.594, 0.734, and 0.66, respectively, which is comparable to those of radiologist with eight years of experience in head CT interpretation.</div></div><div><h3>Conclusion</h3><div>The deep learning model developed from the constructed RSNA 2019+ dataset exhibited good potential in identifying and localizing the five ICH subtypes in CT slices and has the potential to assist in the clinical diagnosis.</div></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"5 1","pages":"Pages 14-22"},"PeriodicalIF":4.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Current trends and future orientation in diagnosing lung pathologies: A systematic survey","authors":"Tamim M. Al-Hasan ,&nbsp;Mohammad Noorizadeh ,&nbsp;Faycal Bensaali ,&nbsp;Nader Meskin ,&nbsp;Ali Ait Hssain","doi":"10.1016/j.imed.2024.09.004","DOIUrl":"10.1016/j.imed.2024.09.004","url":null,"abstract":"<div><div>Lung diseases pose a significant threat to public health worldwide, resulting in a substantial number of fatalities. Diseases such as chronic obstructive pulmonary disease and lung cancer constitute two of the three deadliest diseases worldwide, contributing to over 3 million deaths annually. This study offered a comparative analysis of different diagnostic techniques used for lung pathologies from an engineering standpoint. The review concentrated on intelligent detection methods, including electronic nose, computer vision (CV), or image processing, and biosensors such as graphene-field effect transistor (FET). The E-nose-based detection technique uses electronic sensors to recognize volatile organic compounds (VOCs) in the exhaled breath. These VOCs can aid in the diagnosis of lung pathologies such as pneumonia. The CV processing method involves the application of advanced imaging techniques and machine learning algorithms to scrutinize and diagnose lung pathologies and ventilator-associated pneumonia (VAP). Lastly, biosensors employ the exceptional properties of these materials to identify specific biomarkers in biological samples. This information can be used to diagnose lung pathologies and VAP. This study examined the current state-of-the-art methods and offers a comprehensive analysis of their advantages and disadvantages from an engineering perspective. The study underscored the potential of these techniques to enhance the diagnosis of lung pathologies and VAP and presents the advances in the field of smart biomedical applications. Additionally, it emphasized the necessity for further research to optimize their performance and clinical usefulness.</div></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"5 1","pages":"Pages 23-36"},"PeriodicalIF":4.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning model for predicting corneal stiffness and identifying keratoconus based on ocular structures","authors":"Longhui Li ,&nbsp;Yifan Xiang ,&nbsp;Xi Chen ,&nbsp;Duoru Lin ,&nbsp;Lanqin Zhao ,&nbsp;Jun Xiao ,&nbsp;Zhenzhe Lin ,&nbsp;Jianyu Pang ,&nbsp;Xiaotong Han ,&nbsp;Lixue Liu ,&nbsp;Yuxuan Wu ,&nbsp;Zhenzhen Liu ,&nbsp;Jingjing Chen ,&nbsp;Jing Zhuang ,&nbsp;Keming Yu ,&nbsp;Haotian Lin","doi":"10.1016/j.imed.2024.09.006","DOIUrl":"10.1016/j.imed.2024.09.006","url":null,"abstract":"<div><h3>Background</h3><div>Corneal stiffness abnormalities play an important role in the onset and progression of keratoconus. However, the limited availability of specialty devices for measuring corneal stiffness restricts their application in clinical practice. This study aimed to develop a machine learning (ML) model that can predict corneal stiffness based on ocular structures and investigate its efficacy in diagnosing keratoconus.</div></div><div><h3>Methods</h3><div>This retrospective study enrolled healthy individuals and keratoconus patients at the Zhongshan Ophthalmic Center from June 2018 to June 2021. Eleven features, including ocular structural parameters, intraocular pressure (IOP), and age were used to train ML regression models for predicting the stiffness parameter at first applanation (SP-A1) and the Corvis biomechanical index for Chinese populations (cCBI) measured by a Corvis ST device. Mean absolute errors (MAEs) and the area under the receiver operating characteristic curve (AUC) were used to evaluate the performance of the models. The diagnostic efficacy of the predicted SP-A1 and cCBI for keratoconus was evaluated by the AUC, net reclassification index (NRI), and integrated discrimination improvement (IDI).</div></div><div><h3>Results</h3><div>A total of 1,523 eyes were involved, of which 601 were diagnosed with keratoconus. The MAEs of the SP-A1 prediction were similar in cross-validation (8.95 mmHg/mm) and testing (10.65 mmHg/mm). The R² value for the SP-A1 prediction exceeded 0.7, indicating that the performance was clinically acceptable. The AUC for the cCBI prediction was 0.935 (95% CI 0.906-0.963). The top three predictors for SP-A1 and cCBI were IOP, keratometry, and central corneal thickness. The addition of the predicted SP-A1 and cCBI significantly improved model performance in diagnosing keratoconus, with NRI of 0.607 (95% CI 0.367-0.812) and 0.188 (95% CI −0.022-0.398), and IDI of 0.028 (95% CI 0.006-0.048) and 0.045 (95% CI 0.018-0.072), respectively.</div></div><div><h3>Conclusion</h3><div>Our models predicted SP-A1 and cCBI relatively accurately in keratoconus and normal corneas. Moreover, the predicted SP-A1 and cCBI values significantly contributed to the diagnosis of keratoconus. These models could provide a potential alternative for evaluating corneal stiffness and thus facilitate keratoconus screening.</div></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"5 1","pages":"Pages 66-72"},"PeriodicalIF":4.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large language models-powered clinical decision support: enhancing or replacing human expertise?","authors":"Jia Li,&nbsp;Zichun Zhou,&nbsp;Han Lyu,&nbsp;Zhenchang Wang","doi":"10.1016/j.imed.2025.01.001","DOIUrl":"10.1016/j.imed.2025.01.001","url":null,"abstract":"<div><div>This editorial presents an optimistic yet cautious perspective on the development, deployment, and regulation of large language models (LLMs) in the field of medicine. It is essential to strike a balance between embracing the benefits of artificial intelligence-driven solutions and preserving the human touch that is vital for providing compassionate care. The exponential growth of medical data has paved the way for the integration of LLMs into healthcare, offering unprecedented opportunities to enhance clinical decision-making and alleviate physicians' workloads. Recently, LLMs have exhibited remarkable potential across various clinical scenarios, including streamlining diagnostic processes, optimizing radiology reports, and providing personalized treatment recommendations. However, the implementation of LLMs in healthcare is not without its challenges. Issues such as the scarcity of high-quality annotated data, privacy concerns, and the risk of generating misleading or overconfident information are significant hurdles that must be addressed. Moreover, while LLMs can replace certain basic tasks traditionally performed by humans, it is crucial to recognize that senior clinicians play an irreplaceable role in complex decision-making and providing emotional support to patients. By harnessing the power of LLMs to augment human capabilities while maintaining essential human elements within healthcare, we might shape a future where artificial intelligence and human intelligence coexist harmoniously. Prioritizing ethical development and deployment for artificial intelligence, empowering healthcare professionals, and safeguarding patient privacy will be key to realizing the full potential of LLMs in revolutionizing healthcare delivery. Through ongoing research, collaboration, and adaptation, responsible integration of LLMs holds promise for elevating both quality and accessibility globally, ultimately creating a more efficient, personalized, and patient-centric healthcare system.</div></div>","PeriodicalId":73400,"journal":{"name":"Intelligent medicine","volume":"5 1","pages":"Pages 1-4"},"PeriodicalIF":4.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}