USING ARTIFICIAL INTELLIGENCE FOR BIOMARKER ANALYSIS IN CLINICAL DIAGNOSTICS DOI
П. В. Селиверстов, V. Kutsenko,

V. G. Gorelova

et al.

Molekulyarnaya Meditsina (Molecular medicine), Journal Year: 2024, Volume and Issue: unknown, P. 31 - 40

Published: Nov. 6, 2024

Introduction. Artificial intelligence (AI) technologies are becoming crucial in clinical diagnostics due to their ability process and interpret large volumes of data. The implementation AI for biomarker analysis opens new opportunities personalized medicine, offering more accurate individualized approaches disease diagnosis treatment. relevance this review stems from the need systematize recent advances application analysis, which is critical early prediction chronic non-communicable diseases (NCDs). Material methods. peer-reviewed scientific publications reports leading research centers over past five years was conducted. Studies on algorithms analyzing genomic, proteomic, metabolomic biomarkers were reviewed, including machine learning methods deep neural networks. Special attention paid integration multi-marker panels improving accuracy cardiovascular, digestive, respiratory, endocrine system diseases, as well oncological neurodegenerative pathologies. Results. has significantly increased sensitivity specificity diagnostics, especially complex cases requiring multiple parameters. effectiveness been demonstrated lung, breast, colorectal cancer, cardiovascular complications NCDs progression, diabetes mellitus Alzheimer’s disease. AI’s significant contribution discovery biomarkers, optimization treatment, improvement therapeutic strategies noted. Conclusion. use become a breakthrough medical particularly oncology, cardiology, diseases. technology allows data about various contributes creating models prediction. Further development associated with advancement overcoming ethical regulatory barriers, will expand capabilities practice.

Language: Английский

Exploring Artificial Intelligence Biases in Predictive Models for Cancer Diagnosis DOI Open Access
Aref Smiley, C. Mahony Reátegui-Rivera, David Villarreal‐Zegarra

et al.

Cancers, Journal Year: 2025, Volume and Issue: 17(3), P. 407 - 407

Published: Jan. 26, 2025

The American Society of Clinical Oncology (ASCO) has released the principles for responsible use artificial intelligence (AI) in oncology emphasizing fairness, accountability, oversight, equity, and transparency. However, extent to which these are followed is unknown. goal this study was assess presence biases quality studies on AI models according ASCO examine their potential impact through citation analysis subsequent research applications. A review original articles centered evaluation predictive cancer diagnosis published journal dedicated informatics data science clinical conducted. Seventeen bias criteria were used evaluate sources studies, aligned with ASCO’s oncology. CREMLS checklist applied quality, focusing reporting standards, performance metrics along counts included analyzed. Nine included. most common environmental life-course bias, contextual provider expertise implicit bias. Among principles, least adhered transparency, oversight privacy, human-centered application. Only 22% provided access data. revealed deficiencies methodology reporting. Most reported within moderate high ranges. Additionally, two replicated research. In conclusion, exhibited various types deficiencies, failure adhere oncology, limiting applicability reproducibility. Greater accessibility, compliance international guidelines recommended improve reliability AI-based

Language: Английский

Citations

3
Integrating AI into Cancer Immunotherapy—A Narrative Review of Current Applications and Future Directions DOI Creative Commons
David B. Olawade, Aanuoluwapo Clement David-Olawade, Temitope Adereni

et al.

Diseases, Journal Year: 2025, Volume and Issue: 13(1), P. 24 - 24

Published: Jan. 20, 2025

Background: Cancer remains a leading cause of morbidity and mortality worldwide. Traditional treatments like chemotherapy radiation often result in significant side effects varied patient outcomes. Immunotherapy has emerged as promising alternative, harnessing the immune system to target cancer cells. However, complexity responses tumor heterogeneity challenges its effectiveness. Objective: This mini-narrative review explores role artificial intelligence [AI] enhancing efficacy immunotherapy, predicting responses, discovering novel therapeutic targets. Methods: A comprehensive literature was conducted, focusing on studies published between 2010 2024 that examined application AI immunotherapy. Databases such PubMed, Google Scholar, Web Science were utilized, articles selected based relevance topic. Results: significantly contributed identifying biomarkers predict immunotherapy by analyzing genomic, transcriptomic, proteomic data. It also optimizes combination therapies most effective treatment protocols. AI-driven predictive models help assess response guiding clinical decision-making minimizing effects. Additionally, facilitates discovery targets, neoantigens, enabling development personalized immunotherapies. Conclusions: holds immense potential transforming related data privacy, algorithm transparency, integration must be addressed. Overcoming these hurdles will likely make central component future offering more treatments.

Language: Английский

Citations

1
Artificial Intelligence for Drug Discovery: An Update and Future Prospects DOI
Harrison Howell, Jeremy McGale,

Aurélie Choucair

et al.

Seminars in Nuclear Medicine, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 1, 2025

Language: Английский

Citations

1
A Holistic Approach to Implementing Artificial Intelligence in Lung Cancer DOI

Seyed Masoud HaghighiKian,

Ahmad Shirinzadeh-Dastgiri,

Mohammad Vakili-Ojarood

et al.

Indian Journal of Surgical Oncology, Journal Year: 2024, Volume and Issue: 16(1), P. 257 - 278

Published: Sept. 5, 2024

Language: Английский

Citations

7
The future insights of AI Applications in Hematology diseases diagnosis and prognosis: Review Article DOI
Hisham Ali Waggiallah,

Abdulkareem Al-Garni,

Aisha Ali M Ghazwani

et al.

Salud Ciencia y Tecnología, Journal Year: 2025, Volume and Issue: 5, P. 1430 - 1430

Published: Feb. 13, 2025

Artificial intelligence (AI) is rapidly altering the field of hematology, providing novel approaches to diagnosis, prognosis, and management hematological illnesses. AI technologies, including machine learning (ML) deep (DL), allow for analysis massive volumes clinical, genetic, imaging data, resulting in more accurate, rapid, individualized care. In diagnostic transforming blood smear analysis, bone marrow aspirations, genomic profiling by automating cell classification, detecting anomalies, discovering critical genetic changes associated with AI-powered models are also improving prognostic skills predicting disease progression, treatment response, risk relapse illnesses such as leukemia, lymphoma, anemia, myeloproliferative disorders. Furthermore, applications precision medicine enable clinicians adapt medicines based on individual profiles, thereby increasing therapeutic success reducing unwanted effects. The combination modern technology wearable health monitors real-time tools promises improve patient proactive care via continuous monitoring adaptive options. As develops, it has enormous potential enabling early identification, optimizing regimens, ultimately survival quality life. This study investigates future implications emphasizing their revolutionary impact techniques.

Language: Английский

Citations

0
Smart nanomedicines powered by artificial intelligence: a breakthrough in lung cancer diagnosis and treatment DOI

Moloudosadat Alavinejad,

Moein Shirzad,

Mohammad Javad Javid-Naderi

et al.

Medical Oncology, Journal Year: 2025, Volume and Issue: 42(5)

Published: March 25, 2025

Language: Английский

Citations

0
The Role of Eosinophils, Eosinophil-Related Cytokines and AI in Predicting Immunotherapy Efficacy in NSCLC Cancer DOI Creative Commons

Fausto Omero,

Desirèe Speranza, Giuseppe Murdaca

et al.

Biomolecules, Journal Year: 2025, Volume and Issue: 15(4), P. 491 - 491

Published: March 27, 2025

Immunotherapy and chemoimmunotherapy are standard treatments for non-oncogene-addicted advanced non-small cell lung cancer (NSCLC). Currently, a limited number of biomarkers, including programmed death-ligand 1 (PD-L1) expression, microsatellite instability (MSI), tumor mutational burden (TMB), used in clinical practice to predict benefits from immune checkpoint inhibitors (ICIs). It is therefore necessary search novel biomarkers that could be helpful identify patients who respond immunotherapy. In this context, research efforts focusing on different cells mechanisms involved anti-tumor response. Herein, we provide un updated literature review the role eosinophils development response, functions some cytokines, IL-31 IL-33, eosinophil activation. We discuss available data demonstrating correlation between outcomes ICIs cancer. underscore absolute count (AEC) tumor-associated tissue eosinophilia (TATE) as promising able efficacy toxicities The cytokines NSCLC, treated with ICIs, not yet fully understood, further may crucial determine their Artificial intelligence, through analysis big data, exploited future elucidate

Language: Английский

Citations

0
Artificial Intelligence in Thoracic Surgery: A Review Bridging Innovation and Clinical Practice for the Next Generation of Surgical Care DOI Open Access
Vasileios Leivaditis,

Andreas Maniatopoulos,

Henning Lausberg

et al.

Journal of Clinical Medicine, Journal Year: 2025, Volume and Issue: 14(8), P. 2729 - 2729

Published: April 16, 2025

Background: Artificial intelligence (AI) is rapidly transforming thoracic surgery by enhancing diagnostic accuracy, surgical precision, intraoperative guidance, and postoperative management. AI-driven technologies, including machine learning (ML), deep learning, computer vision, robotic-assisted surgery, have the potential to optimize clinical workflows improve patient outcomes. However, challenges such as data integration, ethical concerns, regulatory barriers must be addressed ensure AI’s safe effective implementation. This review aims analyze current applications, benefits, limitations, future directions of AI in surgery. Methods: was conducted following Preferred Reporting Items for Systematic Reviews Meta-Analyses (PRISMA) guidelines. A comprehensive literature search performed using PubMed, Scopus, Web Science, Cochrane Library studies published up January 2025. Relevant articles were selected based on predefined inclusion exclusion criteria, focusing applications diagnostics, care. risk bias assessment Risk Bias Tool ROBINS-I non-randomized studies. Results: Out 279 identified studies, 36 met criteria qualitative synthesis, highlighting growing role care imaging analysis radiomics improved pulmonary nodule detection, lung cancer classification, lymph node metastasis prediction, while (RATS) has enhanced reduced operative times, recovery rates. Intraoperatively, AI-powered image-guided navigation, augmented reality (AR), real-time decision-support systems optimized planning safety. Postoperatively, predictive models wearable monitoring devices enabled early complication detection follow-up. remain, algorithmic biases, a lack multicenter validation, high implementation costs, concerns regarding security accountability. Despite these shown significant enhance outcomes, requiring further research standardized validation widespread adoption. Conclusions: poised revolutionize decision-making, improving optimizing workflows. adoption requires addressing key limitations through frameworks, governance. Future should focus digital twin technology, federated explainable (XAI) interpretability, reliability, accessibility. With continued advancements responsible will play pivotal shaping next generation precision

Language: Английский

Citations

0
A Comprehensive Review on the Application of Artificial Intelligence for Predicting Postsurgical Recurrence Risk in Early‐Stage Non‐Small Cell Lung Cancer Using Computed Tomography, Positron Emission Tomography, and Clinical Data DOI Creative Commons
Ghazal Mehri-Kakavand, Sibusiso Mdletshe, Alan Wang

et al.

Journal of Medical Radiation Sciences, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 23, 2025

ABSTRACT Introduction Non‐small cell lung cancer (NSCLC) is the leading cause of cancer‐related mortality worldwide. Despite advancements in early detection and treatment, postsurgical recurrence remains a significant challenge, occurring 30%–55% patients within 5 years after surgery. This review analysed existing studies on utilisation artificial intelligence (AI), incorporating CT, PET, clinical data, for predicting risk early‐stage NSCLCs. Methods A literature search was conducted across multiple databases, focusing published between 2018 2024 that employed radiomics, machine learning, deep learning based preoperative positron emission tomography (PET), computed (CT), PET/CT, with or without data integration. Sixteen met inclusion criteria were assessed methodological quality using METhodological RadiomICs Score (METRICS). Results The reviewed demonstrated potential radiomics AI models postoperative risk. Various approaches showed promising results, including handcrafted features, models, multimodal combining different imaging modalities data. However, several challenges limitations identified, such as small sample sizes, lack external validation, interpretability issues, need effective techniques. Conclusions Future research should focus conducting larger, prospective, multicentre studies, improving integration interpretability, enhancing fusion modalities, assessing utility, standardising methodologies, fostering collaboration among researchers institutions. Addressing these aspects will advance development robust generalizable NSCLC, ultimately patient care outcomes.

Language: Английский

Citations

0
Effectiveness of data-augmentation on deep learning in evaluating rapid on-site cytopathology at endoscopic ultrasound-guided fine needle aspiration DOI Creative Commons

Yuki Fujii,

Daisuke Uchida, Ryosuke Sato

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Sept. 28, 2024

Language: Английский

Citations

2