Literature DB >> 34535775

Artificial intelligence in cancer research, diagnosis and therapy.

Olivier Elemento1, Christina Leslie2, Johan Lundin3,4,5, Georgia Tourassi6.   

Abstract

STANDFIRST: Artificial intelligence and machine learning techniques are breaking into biomedical research and health care, which importantly includes cancer research and oncology, where the potential applications are vast. These include detection and diagnosis of cancer, subtype classification, optimization of cancer treatment and identification of new therapeutic targets in drug discovery. While big data used to train machine learning models may already exist, leveraging this opportunity to realize the full promise of artificial intelligence in both the cancer research space and the clinical space will first require significant obstacles to be surmounted. In this Viewpoint article, we asked four experts for their opinions on how we can begin to implement artificial intelligence while ensuring standards are maintained so as transform cancer diagnosis and the prognosis and treatment of patients with cancer and to drive biological discovery.
© 2021. ©UT-Battelle, LLC, under exclusive licence to Springer Nature Limited 2021.

Entities:  

Mesh:

Year:  2021        PMID: 34535775     DOI: 10.1038/s41568-021-00399-1

Source DB:  PubMed          Journal:  Nat Rev Cancer        ISSN: 1474-175X            Impact factor:   60.716


  9 in total

1.  A handheld intelligent single-molecule binary bioelectronic system for fast and reliable immunometric point-of-care testing.

Authors:  Eleonora Macchia; Zsolt M Kovács-Vajna; Daniela Loconsole; Lucia Sarcina; Massimiliano Redolfi; Maria Chironna; Fabrizio Torricelli; Luisa Torsi
Journal:  Sci Adv       Date:  2022-07-06       Impact factor: 14.957

2.  Swarm learning for decentralized artificial intelligence in cancer histopathology.

Authors:  Oliver Lester Saldanha; Philip Quirke; Nicholas P West; Jacqueline A James; Maurice B Loughrey; Heike I Grabsch; Manuel Salto-Tellez; Elizabeth Alwers; Didem Cifci; Narmin Ghaffari Laleh; Tobias Seibel; Richard Gray; Gordon G A Hutchins; Hermann Brenner; Marko van Treeck; Tanwei Yuan; Titus J Brinker; Jenny Chang-Claude; Firas Khader; Andreas Schuppert; Tom Luedde; Christian Trautwein; Hannah Sophie Muti; Sebastian Foersch; Michael Hoffmeister; Daniel Truhn; Jakob Nikolas Kather
Journal:  Nat Med       Date:  2022-04-25       Impact factor: 87.241

Review 3.  Application Status and Prospects of Artificial Intelligence in Peptic Ulcers.

Authors:  Peng-Yue Zhao; Ke Han; Ren-Qi Yao; Chao Ren; Xiao-Hui Du
Journal:  Front Surg       Date:  2022-06-16

Review 4.  The future of early cancer detection.

Authors:  Rebecca C Fitzgerald; Antonis C Antoniou; Ljiljana Fruk; Nitzan Rosenfeld
Journal:  Nat Med       Date:  2022-04-19       Impact factor: 87.241

Review 5.  Challenges and the Evolving Landscape of Assessing Blood-Based PD-L1 Expression as a Biomarker for Anti-PD-(L)1 Immunotherapy.

Authors:  Tao Wang; Desirée Denman; Silvia M Bacot; Gerald M Feldman
Journal:  Biomedicines       Date:  2022-05-20

6.  Construction and Research of Constructive English Teaching Model Applying Multimodal Neural Network Algorithm.

Authors:  Nan Zhang; Hao Wang
Journal:  Comput Intell Neurosci       Date:  2022-05-26

7.  Researchers turn to deep learning to decode protein structures.

Authors:  Stephen Ornes
Journal:  Proc Natl Acad Sci U S A       Date:  2022-03-02       Impact factor: 12.779

Review 8.  Big data in basic and translational cancer research.

Authors:  Peng Jiang; Sanju Sinha; Kenneth Aldape; Sridhar Hannenhalli; Cenk Sahinalp; Eytan Ruppin
Journal:  Nat Rev Cancer       Date:  2022-09-05       Impact factor: 69.800

9.  Lessons for Oncology From the COVID-19 Pandemic: Operationalizing and Scaling Virtual Cancer Care in Health Systems.

Authors:  Thomas J Roberts; Inga T Lennes
Journal:  Cancer J       Date:  2022 Mar-Apr 01       Impact factor: 2.074

  9 in total

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