Exploring the Potential of Digital Twins in Cancer Treatment: A Narrative Review of Reviews DOI Open Access
Daniele Giansanti, Sandra Morelli

Journal of Clinical Medicine, Год журнала: 2025, Номер 14(10), С. 3574 - 3574

Опубликована: Май 20, 2025

Background: Digital twin (DT) technology, integrated with artificial intelligence (AI) and machine learning (ML), holds significant potential to transform oncology care. By creating dynamic virtual replicas of patients, DTs allow clinicians simulate disease progression treatment responses, offering a personalized approach cancer treatment. Aim: This narrative review aimed synthesize existing studies on the application digital twins in oncology, focusing their benefits, challenges, ethical considerations. Methods: The reviews (NRR) followed structured selection process using standardized checklist. Searches were conducted PubMed Scopus predefined query oncology. Reviews prioritized based synthesis prior studies, focus Studies evaluated quality parameters (clear rationale, research design, methodology, results, conclusions, conflict disclosure). Only scores above prefixed threshold disclosed conflicts interest included final synthesis; seventeen selected. Results Discussion: offer advantages such as enhanced decision-making, optimized regimens, improved clinical trial design. Moreover, economic forecasts suggest that integration into healthcare systems may significantly reduce costs drive growth precision medicine market. However, challenges include data issues, complexity biological modeling, need for robust computational resources. A comparison cutting-edge contributes this direction confirms also legal considerations, particularly concerning AI, privacy, accountability, remain barriers. Conclusions: great promise, but requires careful attention ethical, legal, operational challenges. Multidisciplinary efforts, supported by evolving regulatory frameworks like those EU, are essential ensuring responsible effective implementation improve patient outcomes.

Язык: Английский

A vision to the future: value-based laboratory medicine DOI

Mario Plebani,

Janne Cadamuro, Pieter Vermeersch

и другие.

Clinical Chemistry and Laboratory Medicine (CCLM), Год журнала: 2024, Номер 62(12), С. 2373 - 2387

Опубликована: Сен. 11, 2024

Abstract The ultimate goal of value-based laboratory medicine is maximizing the effectiveness tests in improving patient outcomes, optimizing resources and minimizing unnecessary costs. This approach abandons oversimplified notion test volume cost, favor emphasizing clinical utility quality diagnostic decision-making. Several key elements characterize medicine, which can be summarized some basic concepts, such as organization vitro diagnostics (including appropriateness, integrated diagnostics, networking, remote monitoring, disruptive innovations), translation data into information measurable sustainability, reimbursement, ethics (e.g., empowerment safety, protection, analysis big data, scientific publishing). Education training are also crucial, along with considerations for future profession, will largely influenced by advances automation, technology, artificial intelligence, regulations concerning diagnostics. collective opinion paper, composed summaries from presentations given at two-day European Federation Laboratory Medicine (EFLM) Strategic Conference “A vision to future: medicine” (Padova, Italy; September 23–24, 2024), aims provide a comprehensive overview projecting profession more clinically effective sustainable future.

Язык: Английский

Процитировано

10
Enhancing Brain Tumor Detection and Diagnosis DOI

G. Kothai,

B. Sivakarthick,

K. Vignesh

и другие.

Advances in computational intelligence and robotics book series, Год журнала: 2025, Номер unknown, С. 473 - 500

Опубликована: Март 7, 2025

Brain tumors present significant challenges in early detection and precise diagnosis, which are critical for improving patient outcomes. This chapter explores advanced methods enhancing brain tumor diagnosis using image processing convolutional neural networks (CNNs). It delves into the limitations of traditional diagnostic methods, highlighting their lack sensitivity specificity. The integration techniques CNNs is presented as a more effective approach segmentation, classification, improved accuracy. also discusses potential AI-driven systems real-time personalized treatment plans, long-term monitoring. Through comprehensive analysis CNN architectures medical this work emphasizes importance technologies achieving precision healthcare neuro-oncology.

Язык: Английский

Процитировано

0
A systematic review of AI as a digital twin for prostate cancer care DOI Creative Commons

A. Katzenellenbogen John,

Reda Alhajj, Jon Rokne

и другие.

Computer Methods and Programs in Biomedicine, Год журнала: 2025, Номер 268, С. 108804 - 108804

Опубликована: Май 6, 2025

Язык: Английский

Процитировано

0
Exploring the Potential of Digital Twins in Cancer Treatment: A Narrative Review of Reviews DOI Open Access
Daniele Giansanti, Sandra Morelli

Journal of Clinical Medicine, Год журнала: 2025, Номер 14(10), С. 3574 - 3574

Опубликована: Май 20, 2025

Background: Digital twin (DT) technology, integrated with artificial intelligence (AI) and machine learning (ML), holds significant potential to transform oncology care. By creating dynamic virtual replicas of patients, DTs allow clinicians simulate disease progression treatment responses, offering a personalized approach cancer treatment. Aim: This narrative review aimed synthesize existing studies on the application digital twins in oncology, focusing their benefits, challenges, ethical considerations. Methods: The reviews (NRR) followed structured selection process using standardized checklist. Searches were conducted PubMed Scopus predefined query oncology. Reviews prioritized based synthesis prior studies, focus Studies evaluated quality parameters (clear rationale, research design, methodology, results, conclusions, conflict disclosure). Only scores above prefixed threshold disclosed conflicts interest included final synthesis; seventeen selected. Results Discussion: offer advantages such as enhanced decision-making, optimized regimens, improved clinical trial design. Moreover, economic forecasts suggest that integration into healthcare systems may significantly reduce costs drive growth precision medicine market. However, challenges include data issues, complexity biological modeling, need for robust computational resources. A comparison cutting-edge contributes this direction confirms also legal considerations, particularly concerning AI, privacy, accountability, remain barriers. Conclusions: great promise, but requires careful attention ethical, legal, operational challenges. Multidisciplinary efforts, supported by evolving regulatory frameworks like those EU, are essential ensuring responsible effective implementation improve patient outcomes.

Язык: Английский

Процитировано

0