Cargando…

Explainable vs. interpretable artificial intelligence frameworks in oncology

Detalles Bibliográficos
Autores principales:	Bertsimas, Dimitris, Margonis, Georgios Antonios
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	AME Publishing Company 2023
Materias:	Editorial
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10007880/ https://www.ncbi.nlm.nih.gov/pubmed/36915595 http://dx.doi.org/10.21037/tcr-22-2427

Ejemplares similares

Gene Alterations, Mediators, and Artificial Intelligence in Colorectal Liver Metastases
por: Wagner, Doris, et al.
Publicado: (2022)

A framework for artificial intelligence in cancer research and precision oncology
por: Perez-Lopez, Raquel, et al.
Publicado: (2023)

Toward an Optimized Staging System for Pancreatic Ductal Adenocarcinoma: A Clinically Interpretable, Artificial Intelligence–Based Model
por: Bertsimas, Dimitris, et al.
Publicado: (2021)

Integrated multimodal artificial intelligence framework for healthcare applications
por: Soenksen, Luis R., et al.
Publicado: (2022)

Utilization of model-agnostic explainable artificial intelligence frameworks in oncology: a narrative review
por: Ladbury, Colton, et al.
Publicado: (2022)

Interpretable artificial intelligence in radiology and radiation oncology
por: Cui, Sunan, et al.
Publicado: (2023)

Is prognosis uniformly excellent in patients with stage II MSI-high colon cancer?
por: Wang, Jane, et al.
Publicado: (2022)

Artificial intelligence and radiomics for radiation oncology
por: Kocher, Martin
Publicado: (2020)

Telenursing and artificial intelligence for oncology nursing
por: Kamei, Tomoko
Publicado: (2022)

Beauty is in the explainable artificial intelligence (XAI) of the “agnostic” beholder
por: Laios, Alexandros, et al.
Publicado: (2023)

Artificial intelligence for the next generation of precision oncology
por: Ballester, Pedro J., et al.
Publicado: (2021)

Explainable artificial intelligence for mental health through transparency and interpretability for understandability
por: Joyce, Dan W., et al.
Publicado: (2023)

Prognostic and Therapeutic Implications of Tumor Biology in Colorectal Liver Metastases
por: Kamphues, Carsten, et al.
Publicado: (2021)

Designing an Interpretability-Based Model to Explain the Artificial Intelligence Algorithms in Healthcare
por: Ennab, Mohammad, et al.
Publicado: (2022)

Artificial Intelligence in Oncology: A Topical Collection in 2022
por: Stadlbauer, Andreas, et al.
Publicado: (2023)

A new era of oncology through artificial intelligence
por: Curioni-Fontecedro, Alessandra
Publicado: (2017)

An explainable artificial intelligence framework for risk prediction of COPD in smokers
por: Wang, Xuchun, et al.
Publicado: (2023)

An interpretable AI model for recurrence prediction after surgery in gastrointestinal stromal tumour: an observational cohort study
por: Bertsimas, Dimitris, et al.
Publicado: (2023)

Interpretable artificial intelligence framework for COVID-19 screening on chest X-rays
por: Tsiknakis, Nikos, et al.
Publicado: (2020)

A Deep Diagnostic Framework Using Explainable Artificial Intelligence and Clustering
por: Thunold, Håvard Horgen, et al.
Publicado: (2023)

Framework for Classifying Explainable Artificial Intelligence (XAI) Algorithms in Clinical Medicine
por: Gniadek, Thomas, et al.
Publicado: (2023)

A deep explainable artificial intelligent framework for neurological disorders discrimination
por: Shahtalebi, Soroosh, et al.
Publicado: (2021)

Implementation of Artificial Intelligence–Assisted Chest X-ray Interpretation: It Is About Time
por: Ram, Sundaresh, et al.
Publicado: (2023)

Future directions on the merge of quantitative imaging and artificial intelligence in radiation oncology
por: Redalen, Kathrine Røe, et al.
Publicado: (2020)

Ethics and Legal Framework for Trustworthy Artificial Intelligence in Vascular Surgery
por: Lareyre, Fabien, et al.
Publicado: (2023)

Comparison of Chest Radiograph Interpretations by Artificial Intelligence Algorithm vs Radiology Residents
por: Wu, Joy T., et al.
Publicado: (2020)

Risk vs. disease: the role of artificial intelligence in avoiding unneeded testing
por: Cademartiri, Filippo, et al.
Publicado: (2022)

Collaboration between explainable artificial intelligence and pulmonologists improves the accuracy of pulmonary function test interpretation
por: Das, Nilakash, et al.
Publicado: (2023)

Artificial intelligence in oncology
por: Shimizu, Hideyuki, et al.
Publicado: (2020)

Editorial: Interpretable and explainable machine learning models in oncology
por: Hrinivich, William Thomas, et al.
Publicado: (2023)

To explain or not to explain?—Artificial intelligence explainability in clinical decision support systems
por: Amann, Julia, et al.
Publicado: (2022)

Towards Precision Oncology: Enhancing Cancer Screening, Diagnosis and Theragnosis Using Artificial Intelligence
por: Tran, William T.
Publicado: (2022)

Taming the chaos?! Using eXplainable Artificial Intelligence (XAI) to tackle the complexity in mental health research
por: Roessner, Veit, et al.
Publicado: (2021)

Explainable artificial intelligence based analysis for interpreting infant fNIRS data in developmental cognitive neuroscience
por: Andreu-Perez, Javier, et al.
Publicado: (2021)

Causability and explainability of artificial intelligence in medicine
por: Holzinger, Andreas, et al.
Publicado: (2019)

Artificial intelligence for ocular oncology
por: Koseoglu, Neslihan Dilruba, et al.
Publicado: (2023)

Artificial Intelligence in Cutaneous Oncology
por: Chu, Yu Seong, et al.
Publicado: (2020)

An overview of artificial intelligence in oncology
por: Farina, Eduardo, et al.
Publicado: (2022)

Artificial intelligence in oncologic imaging
por: Chen, Melissa M., et al.
Publicado: (2022)

Research agenda for using artificial intelligence in health governance: interpretive scoping review and framework
por: Ramezani, Maryam, et al.
Publicado: (2023)

Cannot write session to /tmp/vufind_sessions/sess_f61p8mu7m2k64okmrt51nlh9aa