Results 1 to 10 of about 2,469,259 (413)

Artificial general intelligence for radiation oncology [PDF]

Meta-Radiology, 2023
The emergence of artificial general intelligence (AGI) is transforming radiation oncology. As prominent vanguards of AGI, large language models (LLMs) such as GPT-4 and PaLM 2 can process extensive texts and large vision models (LVMs) such as the Segment
Chenbin Liu, Zhengliang Liu, Jason Holmes, Lu Zhang, Lian Zhang, Yuzhen Ding, Peng Shu, Zihao Wu, Haixing Dai, Yiwei Li, Dinggang Shen, Ninghao Liu, Quanzheng Li, Xiang Li, Dajiang Zhu, Tianming Liu, Wei Liu   +16 more
doaj   +5 more sources

Why "Radiation Oncology" [PDF]

Radiation Oncology, 2006
Radiotherapy continues to be a major treatment for solid tumours and is a cornerstone of modern oncology. The term 'radiation oncology' describes the integration of radiation therapy into the complexity of multi-modal therapy. Over the last ten years the
Camphausen Kevin A, Belka Claus
doaj   +8 more sources

RadOnc-GPT: A Large Language Model for Radiation Oncology [PDF]

arXiv, 2023
This paper presents RadOnc-GPT, a large language model specialized for radiation oncology through advanced tuning methods. RadOnc-GPT was finetuned on a large dataset of radiation oncology patient records from the Mayo Clinic in Arizona. The model employs instruction tuning on three key tasks - generating radiotherapy treatment regimens, determining ...
Holmes, Jason, Li, Quanzheng, Li, Xiang, Li, Yiwei, Liu, Chenbin, Liu, Ninghao, Liu, Tianming, Liu, Wei, Liu, Zhengliang, Patel, Samir H., Shu, Peng, Sio, Terence T., Wang, Peilong, Zhang, Lian, Zhu, Dajiang   +14 more
arxiv   +5 more sources

Evaluating Large Language Models on a Highly-specialized Topic, Radiation Oncology Physics [PDF]

Frontiers in Oncology, 2023
We present the first study to investigate Large Language Models (LLMs) in answering radiation oncology physics questions. Because popular exams like AP Physics, LSAT, and GRE have large test-taker populations and ample test preparation resources in circulation, they may not allow for accurately assessing the true potential of LLMs.
J. Holmes, Zheng Liu, Lian-Cheng Zhang, Yuzhen Ding, T. Sio, L. Mcgee, J. Ashman, Xiang Li, Tianming Liu, Jiajian Shen, W. Liu   +10 more
arxiv   +3 more sources

LLM-driven multimodal target volume contouring in radiation oncology. [PDF]

Nat Commun, 2023
Target volume contouring for radiation therapy is considered significantly more challenging than the normal organ segmentation tasks as it necessitates the utilization of both image and text-based clinical information. Inspired by the recent advancement of large language models (LLMs) that can facilitate the integration of the textural information and ...
Oh Y, Park S, Byun HK, Cho Y, Lee IJ, Kim JS, Ye JC.   +6 more
europepmc   +2 more sources

Machine learning applications in radiation oncology

Physics and Imaging in Radiation Oncology, 2021
Machine learning technology has a growing impact on radiation oncology with an increasing presence in research and industry. The prevalence of diverse data including 3D imaging and the 3D radiation dose delivery presents potential for future automation ...
Matthew Field, Nicholas Hardcastle, Michael Jameson, Noel Aherne, Lois Holloway   +4 more
doaj   +2 more sources

Increasing pediatric radiation oncology capacity in sub-saharan Africa using technology: a pilot of a pediatric radiation oncology virtual training course [PDF]

BMC Medical Education
Background The shortage of skilled healthcare professionals in pediatric oncology and the limited access to training programs remain significant challenges in Nigeria and sub-Saharan Africa. The the Pediatric Radiation Oncology (Virtual) Course, ‘PedROC’
Adedayo O. Joseph, Adeseye M. Akinsete, Azeezat O. Ajose, Aishat T. Oladipo, Adeola Maliki, Korede Akindele, Michelle Mangongolo, Samuel Adeneye, Wilfred Ngwa   +8 more
doaj   +2 more sources

Labeling for Big Data in radiation oncology: The Radiation Oncology Structures ontology. [PDF]

PLoS ONE, 2018
Leveraging Electronic Health Records (EHR) and Oncology Information Systems (OIS) has great potential to generate hypotheses for cancer treatment, since they directly provide medical data on a large scale.
Jean-Emmanuel Bibault, Eric Zapletal, Bastien Rance, Philippe Giraud, Anita Burgun   +4 more
doaj   +6 more sources

The Radiation Oncology NLP Database [PDF]

arXiv
We present the Radiation Oncology NLP Database (ROND), the first dedicated Natural Language Processing (NLP) dataset for radiation oncology, an important medical specialty that has received limited attention from the NLP community in the past. With the advent of Artificial General Intelligence (AGI), there is an increasing need for specialized datasets
Cai, Hongmin, Elahi, Muhammad Ali, Feng, Hongying, Holmes, Jason, Li, Quanzheng, Li, Xiang, Liao, Wenxiong, Liu, Chenbin, Liu, Tianming, Liu, Wei, Liu, Zhengliang, Shen, Jiajian, Sun, Lichao, Wang, Peilong, Zhang, Lian   +14 more
arxiv   +2 more sources

An Emerging Role for Radiation Oncology in Precision Oncology [PDF]

EBioMedicine, 2016
Precision medicine is rapidly evolving in the management of cancer, in an effort to deliver the right therapy to the right patient at the right time. The use of novel molecular or genetic signatures in decision making regarding local–regional management is in its infancy. In their recent work published in EBioMedicine, Cheng and colleagues developed
Sharad Goyal, Bruce G. Haffty
doaj   +4 more sources