Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews DOI Creative Commons
Haishan Xu,

Ting‐Ting Gong,

Xin‐Jian Song

и другие.

Journal of Medical Internet Research, Год журнала: 2025, Номер 27, С. e53567 - e53567

Опубликована: Апрель 1, 2025

Background Artificial intelligence (AI) has the potential to transform cancer diagnosis, ultimately leading better patient outcomes. Objective We performed an umbrella review summarize and critically evaluate evidence for AI-based imaging diagnosis of cancers. Methods PubMed, Embase, Web Science, Cochrane, IEEE databases were searched relevant systematic reviews from inception June 19, 2024. Two independent investigators abstracted data assessed quality evidence, using Joanna Briggs Institute (JBI) Critical Appraisal Checklist Systematic Reviews Research Syntheses. further in each meta-analysis by applying Grading Recommendations, Assessment, Development, Evaluation (GRADE) criteria. Diagnostic performance synthesized narratively. Results In a comprehensive analysis 158 included studies evaluating AI algorithms noninvasive across 8 major human system cancers, accuracy classifiers central nervous cancers varied widely (ranging 48% 100%). Similarities observed diagnostic head neck, respiratory system, digestive urinary female-related systems, skin, other sites. Most meta-analyses demonstrated positive summary performance. For instance, 9 meta-analyzed sensitivity specificity esophageal cancer, showing ranges 90%-95% 80%-93.8%, respectively. case breast detection, calculated pooled within 75.4%-92% 83%-90.6%, Four reported ovarian both 75%-94%. Notably, lung was relatively low, primarily distributed between 65% 80%. Furthermore, 80.4% (127/158) high according JBI Checklist, with remaining classified as medium quality. The GRADE assessment indicated that overall moderate low. Conclusions Although shows great achieving accelerated, accurate, more objective diagnoses multiple there are still hurdles overcome before its implementation clinical settings. present findings highlight concerted effort research community, clinicians, policymakers is required existing translate this into improved outcomes health care delivery. Trial Registration PROSPERO CRD42022364278; https://www.crd.york.ac.uk/PROSPERO/view/CRD42022364278

Язык: Английский

Artificial Intelligence and Lung Cancer: Impact on Improving Patient Outcomes DOI Open Access
Zainab Gandhi, Priyatham Gurram,

Birendra Amgai

и другие.

Cancers, Год журнала: 2023, Номер 15(21), С. 5236 - 5236

Опубликована: Окт. 31, 2023

Lung cancer remains one of the leading causes cancer-related deaths worldwide, emphasizing need for improved diagnostic and treatment approaches. In recent years, emergence artificial intelligence (AI) has sparked considerable interest in its potential role lung cancer. This review aims to provide an overview current state AI applications screening, diagnosis, treatment. algorithms like machine learning, deep radiomics have shown remarkable capabilities detection characterization nodules, thereby aiding accurate screening diagnosis. These systems can analyze various imaging modalities, such as low-dose CT scans, PET-CT imaging, even chest radiographs, accurately identifying suspicious nodules facilitating timely intervention. models exhibited promise utilizing biomarkers tumor markers supplementary tools, effectively enhancing specificity accuracy early detection. distinguish between benign malignant assisting radiologists making more informed decisions. Additionally, hold integrate multiple modalities clinical data, providing a comprehensive assessment. By high-quality including patient demographics, history, genetic profiles, predict responses guide selection optimal therapies. Notably, these success predicting likelihood response recurrence following targeted therapies optimizing radiation therapy patients. Implementing tools practice aid diagnosis management potentially improve outcomes, mortality morbidity

Язык: Английский

Процитировано

43
Convergence of evolving artificial intelligence and machine learning techniques in precision oncology DOI Creative Commons
Elena Fountzilas, Tillman Pearce, Mehmet A. Baysal

и другие.

npj Digital Medicine, Год журнала: 2025, Номер 8(1)

Опубликована: Янв. 31, 2025

The confluence of new technologies with artificial intelligence (AI) and machine learning (ML) analytical techniques is rapidly advancing the field precision oncology, promising to improve diagnostic approaches therapeutic strategies for patients cancer. By analyzing multi-dimensional, multiomic, spatial pathology, radiomic data, these enable a deeper understanding intricate molecular pathways, aiding in identification critical nodes within tumor's biology optimize treatment selection. applications AI/ML oncology are extensive include generation synthetic e.g., digital twins, order provide necessary information design or expedite conduct clinical trials. Currently, many operational technical challenges exist related data technology, engineering, storage; algorithm development structures; quality quantity pipeline; sharing generalizability; incorporation into current workflow reimbursement models.

Язык: Английский

Процитировано

4
AI-Driven Models for Diagnosing and Predicting Outcomes in Lung Cancer: A Systematic Review and Meta-Analysis DOI Open Access
Mohammed Kanan Alshammari, Hajar Alharbi, Nawaf Alotaibi

и другие.

Cancers, Год журнала: 2024, Номер 16(3), С. 674 - 674

Опубликована: Фев. 5, 2024

(1) Background: Lung cancer's high mortality due to late diagnosis highlights a need for early detection strategies. Artificial intelligence (AI) in healthcare, particularly lung cancer, offers promise by analyzing medical data identification and personalized treatment. This systematic review evaluates AI's performance cancer detection, its techniques, strengths, limitations, comparative edge over traditional methods. (2) Methods: meta-analysis followed the PRISMA guidelines rigorously, outlining comprehensive protocol employing tailored search strategies across diverse databases. Two reviewers independently screened studies based on predefined criteria, ensuring selection of high-quality relevant role detection. The extraction key study details metrics, quality assessment, facilitated robust analysis using R software (Version 4.3.0). process, depicted via flow diagram, allowed meticulous evaluation synthesis findings this review. (3) Results: From 1024 records, 39 met inclusion showcasing AI model applications emphasizing varying strengths among studies. These underscore potential but highlight standardization amidst variations. results demonstrate promising pooled sensitivity specificity 0.87, signifying accuracy identifying true positives negatives, despite observed heterogeneity attributed parameters. (4) Conclusions: demonstrates showing levels However, variations underline standardized protocols fully leverage revolutionizing diagnosis, ultimately benefiting patients healthcare professionals. As field progresses, validated models from large-scale perspective will greatly benefit clinical practice patient care future.

Язык: Английский

Процитировано

13
Revolutionizing Cancer Detection: Harnessing Quantum Dots and Graphene-Based Nanobiosensors for Lung and Breast Cancer Diagnosis DOI
Soheil Sadr, Abbas Rahdar, Sadanand Pandey

и другие.

BioNanoScience, Год журнала: 2024, Номер 15(1)

Опубликована: Дек. 13, 2024

Язык: Английский

Процитировано

9
Artificial intelligence‐driven change redefining radiology through interdisciplinary innovation DOI Creative Commons
Runqiu Huang, Xiaolin Meng, Xiaoxuan Zhang

и другие.

Deleted Journal, Год журнала: 2025, Номер unknown

Опубликована: Янв. 6, 2025

Abstract Artificial intelligence (AI) is rapidly advancing, yet its applications in radiology remain relatively nascent. From a spatiotemporal perspective, this review examines the forces driving AI development and integration with medicine radiology, particular focus on advancements addressing major diseases that significantly threaten human health. Temporally, advent of foundational model architectures, combined underlying drivers development, accelerating progress interventions their practical applications. Spatially, discussion explores potential evolving methodologies to strengthen interdisciplinary within medicine, emphasizing four critical points imaging process, as well application disease management, including emergence commercial products. Additionally, current utilization deep learning reviewed, future through multimodal foundation models Generative Pre‐trained Transformer are anticipated.

Язык: Английский

Процитировано

1
Bibliometric analysis and research trends of artificial intelligence in lung cancer DOI Creative Commons
Adem Gencer

Heliyon, Год журнала: 2024, Номер 10(2), С. e24665 - e24665

Опубликована: Янв. 1, 2024

Due to the rapid advancement of technology, artificial intelligence (AI) has become extensively used for diagnosis and prognosis various diseases, such as lung cancer. Research in field literature demonstrated that can be valuable timely detection cancer formulation an effective treatment plan. This study aims conduct a bibliometric analysis examine illustrate specific areas focus, research frontiers, evolutionary processes, trends existing on context

Язык: Английский

Процитировано

7
Artificial intelligence for screening and diagnosis of amyotrophic lateral sclerosis: a systematic review and meta-analysis DOI
Tungki Pratama Umar, Nityanand Jain, Manthia Papageorgakopoulou

и другие.

Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration, Год журнала: 2024, Номер 25(5-6), С. 425 - 436

Опубликована: Апрель 2, 2024

Amyotrophic lateral sclerosis (ALS) is a rare and fatal neurological disease that leads to progressive motor function degeneration. Diagnosing ALS challenging due the absence of specific detection test. The use artificial intelligence (AI) can assist in investigation treatment ALS.

Язык: Английский

Процитировано

6
Standalone deep learning versus experts for diagnosis lung cancer on chest computed tomography: a systematic review DOI Creative Commons
Tingwei Wang, Jia‐Sheng Hong, Hwa‐Yen Chiu

и другие.

European Radiology, Год журнала: 2024, Номер 34(11), С. 7397 - 7407

Опубликована: Май 22, 2024

Abstract Purpose To compare the diagnostic performance of standalone deep learning (DL) algorithms and human experts in lung cancer detection on chest computed tomography (CT) scans. Materials methods This study searched for studies PubMed, Embase, Web Science from their inception until November 2023. We focused adult patients compared efficacy DL expert radiologists disease diagnosis CT Quality assessment was performed using QUADAS-2, QUADAS-C, CLAIM. Bivariate random-effects subgroup analyses were tasks (malignancy classification vs invasiveness classification), imaging modalities (CT low-dose [LDCT] high-resolution CT), region, software used, publication year. Results included 20 various aspects Quantitatively, exhibited superior sensitivity (82%) specificity (75%) to (sensitivity 81%, 69%). However, difference statistically significant, whereas not significant. The algorithms’ varied across different tasks, demonstrating need tailored optimization algorithms. Notably, matched standard CT, surpassing them specificity, but showed higher with lower LDCT Conclusion demonstrated improved accuracy over readers malignancy varies by modality, underlining importance continued research fully assess effectiveness cancer. Clinical relevance statement have potential refine matching specificity. These findings call further modalities, aiming advance clinical diagnostics patient outcomes. Key Points Lung is challenging can be AI integration . shows than Enhanced could lead outcomes

Язык: Английский

Процитировано

6
Systematic review and meta-analysis of deep learning applications in computed tomography lung cancer segmentation DOI
Tingwei Wang, Jia‐Sheng Hong, Jing-Wen Huang

и другие.

Radiotherapy and Oncology, Год журнала: 2024, Номер 197, С. 110344 - 110344

Опубликована: Май 26, 2024

Язык: Английский

Процитировано

6
Unraveling Therapeutic Opportunities and the Diagnostic Potential of microRNAs for Human Lung Cancer DOI Creative Commons
Osama Sweef, Elsayed Zaabout,

Ahmed Bakheet

и другие.

Pharmaceutics, Год журнала: 2023, Номер 15(8), С. 2061 - 2061

Опубликована: Июль 31, 2023

Lung cancer is a major public health problem and leading cause of cancer-related deaths worldwide. Despite advances in treatment options, the five-year survival rate for lung patients remains low, emphasizing urgent need innovative diagnostic therapeutic strategies. MicroRNAs (miRNAs) have emerged as potential biomarkers targets due to their crucial roles regulating cell proliferation, differentiation, apoptosis. For example, miR-34a miR-150, once delivered via liposomes or nanoparticles, can inhibit tumor growth by downregulating critical promoting genes. Conversely, miR-21 miR-155, frequently overexpressed cancer, are associated with increased invasion, chemotherapy resistance. In this review, we summarize current knowledge miRNAs carcinogenesis, especially those induced exposure environmental pollutants, namely, arsenic benzopyrene, which account up 1/10 cases. We then discuss recent miRNA-based therapeutics diagnostics. Such information will provide new insights into pathogenesis modalities based on miRNAs.

Язык: Английский

Процитировано

15