Oncogene, Journal Year: 2023, Volume and Issue: 42(42), P. 3089 - 3097
Published: Sept. 8, 2023
Language: Английский
Nature Medicine, Journal Year: 2022, Volume and Issue: 28(1), P. 31 - 38
Published: Jan. 1, 2022
Language: Английский
Citations
1457
Genome Medicine, Journal Year: 2021, Volume and Issue: 13(1)
Published: Sept. 27, 2021
Abstract Deep learning is a subdiscipline of artificial intelligence that uses machine technique called neural networks to extract patterns and make predictions from large data sets. The increasing adoption deep across healthcare domains together with the availability highly characterised cancer datasets has accelerated research into utility in analysis complex biology cancer. While early results are promising, this rapidly evolving field new knowledge emerging both learning. In review, we provide an overview techniques how they being applied oncology. We focus on applications for omics types, including genomic, methylation transcriptomic data, as well histopathology-based genomic inference, perspectives different types can be integrated develop decision support tools. specific examples may diagnosis, prognosis treatment management. also assess current limitations challenges application precision oncology, lack phenotypically rich need more explainable models. Finally, conclude discussion obstacles overcome enable future clinical utilisation
Language: Английский
Citations
590
British Journal of Cancer, Journal Year: 2021, Volume and Issue: 126(1), P. 4 - 9
Published: Nov. 26, 2021
Abstract Artificial intelligence (AI) is concretely reshaping the landscape and horizons of oncology, opening new important opportunities for improving management cancer patients. Analysing AI-based devices that have already obtained official approval by Federal Drug Administration (FDA), here we show diagnostics oncology-related area in which AI entered with largest impact into clinical practice. Furthermore, breast, lung prostate cancers represent specific types now are experiencing more advantages from devices. The future perspectives oncology discussed: creation multidisciplinary platforms, comprehension importance all neoplasms, including rare tumours continuous support guaranteeing its growth this time most challenges finalising ‘AI-revolution’ oncology.
Language: Английский
Citations
166
Frontiers in Medicine, Journal Year: 2022, Volume and Issue: 9
Published: Aug. 31, 2022
Artificial intelligence (AI) needs to be accepted and understood by physicians medical students, but few have systematically assessed their attitudes. We investigated clinical AI acceptance among students around the world provide implementation guidance.We conducted a two-stage study, involving foundational systematic review of physician student AI. This enabled us design suitable web-based questionnaire which was then distributed practitioners trainees world.Sixty studies were included in this review, 758 respondents from 39 countries completed online questionnaire. Five (62.50%) eight reported 65% or higher awareness regarding application Although, only 10-30% had actually used 26 (74.28%) 35 suggested there lack knowledge. Our uncovered 38% rate 20% utility AI, although 53% lacked basic knowledge Forty-five mentioned attitudes toward over 60% 38 (84.44%) positive about they also concerned potential for unpredictable, incorrect results. Seventy-seven percent optimistic prospect The support statement that could replace ranged 6 78% across 40 topic. recommended efforts should made increase collaboration. showed 68% disagreed would become surrogate physician, believed it assist decision-making. Participants with different identities, experience hold similar subtly attitudes.Most appear aware increasing practical related Overall, participants reserved In spite mixed opinions becoming consensus collaborations between two strengthened. Further education alleviate anxieties associated change adopting new technologies.
Language: Английский
Citations
115
Nature Communications, Journal Year: 2022, Volume and Issue: 13(1)
Published: Nov. 2, 2022
In radiotherapy for cancer patients, an indispensable process is to delineate organs-at-risk (OARs) and tumors. However, it the most time-consuming step as manual delineation always required from radiation oncologists. Herein, we propose a lightweight deep learning framework treatment planning (RTP), named RTP-Net, promote automatic, rapid, precise initialization of whole-body OARs Briefly, implements cascade coarse-to-fine segmentation, with adaptive module both small large organs, attention mechanisms organs boundaries. Our experiments show three merits: 1) Extensively evaluates on 67 tasks large-scale dataset 28,581 cases; 2) Demonstrates comparable or superior accuracy average Dice 0.95; 3) Achieves near real-time in <2 s. This could be utilized accelerate contouring All-in-One scheme, thus greatly shorten turnaround time patients.
Language: Английский
Citations
77
Archives of Gynecology and Obstetrics, Journal Year: 2023, Volume and Issue: 308(6), P. 1831 - 1844
Published: July 17, 2023
As the available information about breast cancer is growing every day, decision-making process for therapy getting more complex. ChatGPT as a transformer-based language model possesses ability to write scientific articles and pass medical exams. But it able support multidisciplinary tumor board (MDT) in planning of patients with cancer?
Language: Английский
Citations
68
Nature Biomedical Engineering, Journal Year: 2023, Volume and Issue: 7(10), P. 1242 - 1251
Published: April 13, 2023
Language: Английский
Citations
48
Chemical Society Reviews, Journal Year: 2023, Volume and Issue: 52(22), P. 7737 - 7772
Published: Jan. 1, 2023
Bioorthogonal chemistry used in prodrug activation for cancer treatment and its potential clinical translation.
Language: Английский
Citations
48
Cancer Discovery, Journal Year: 2024, Volume and Issue: 14(5), P. 711 - 726
Published: March 21, 2024
Artificial intelligence (AI) in oncology is advancing beyond algorithm development to integration into clinical practice. This review describes the current state of field, with a specific focus on integration. AI applications are structured according cancer type and domain, focusing four most common cancers tasks detection, diagnosis, treatment. These encompass various data modalities, including imaging, genomics, medical records. We conclude summary existing challenges, evolving solutions, potential future directions for field.
Language: Английский
Citations
36
Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(4)
Published: March 15, 2024
Abstract Advancements in artificial intelligence (AI) have driven extensive research into developing diverse multimodal data analysis approaches for smart healthcare. There is a scarcity of large-scale literature this field based on quantitative approaches. This study performed bibliometric and topic modeling examination 683 articles from 2002 to 2022, focusing topics trends, journals, countries/regions, institutions, authors, scientific collaborations. Results showed that, firstly, the number has grown 1 220 with majority being published interdisciplinary journals that link healthcare medical information technology AI. Secondly, significant rise quantity can be attributed increasing contribution scholars non-English speaking countries/regions noteworthy contributions made by authors USA India. Thirdly, researchers show high interest issues, especially, cross-modality magnetic resonance imaging (MRI) brain tumor analysis, cancer prognosis through multi-dimensional AI-assisted diagnostics personalization healthcare, each experiencing increase interest. an emerging trend towards issues such as applying generative adversarial networks contrastive learning image fusion synthesis utilizing combined spatiotemporal resolution functional MRI electroencephalogram data-centric manner. valuable enhancing researchers’ practitioners’ understanding present focal points upcoming trajectories AI-powered analysis.
Language: Английский
Citations
25