AIMC Topic: Radiation Oncology

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ChatGPT and research in radiation oncology: Correspondence.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology

NRG Oncology Assessment of Artificial Intelligence Deep Learning-Based Auto-segmentation for Radiation Therapy: Current Developments, Clinical Considerations, and Future Directions.

International journal of radiation oncology, biology, physics
Deep learning neural networks (DLNN) in Artificial intelligence (AI) have been extensively explored for automatic segmentation in radiotherapy (RT). In contrast to traditional model-based methods, data-driven AI-based models for auto-segmentation hav...

Framework for Radiation Oncology Department-wide Evaluation and Implementation of Commercial Artificial Intelligence Autocontouring.

Practical radiation oncology
PURPOSE: Artificial intelligence (AI)-based autocontouring in radiation oncology has potential benefits such as standardization and time savings. However, commercial AI solutions require careful evaluation before clinical integration. We developed a ...

Current Strengths and Weaknesses of ChatGPT as a Resource for Radiation Oncology Patients and Providers.

International journal of radiation oncology, biology, physics
PURPOSE: Chat Generative Pre-Trained Transformer (ChatGPT), an artificial intelligence program that uses natural language processing to generate conversational-style responses to questions or inputs, is increasingly being used by both patients and he...

Automation and artificial intelligence in radiation therapy treatment planning.

Journal of medical radiation sciences
Automation and artificial intelligence (AI) is already possible for many radiation therapy planning and treatment processes with the aim of improving workflows and increasing efficiency in radiation oncology departments. Currently, AI technology is a...

Black box no more: a scoping review of AI governance frameworks to guide procurement and adoption of AI in medical imaging and radiotherapy in the UK.

The British journal of radiology
Technological advancements in computer science have started to bring artificial intelligence (AI) from the bench closer to the bedside. While there is still lots to do and improve, AI models in medical imaging and radiotherapy are rapidly being devel...

Considerations for environmental sustainability in clinical radiology and radiotherapy practice: A systematic literature review and recommendations for a greener practice.

Radiography (London, England : 1995)
INTRODUCTION: Environmental sustainability (ES) in healthcare is an important current challenge in the wider context of reducing the environmental impacts of human activity. Identifying key routes to making clinical radiology and radiotherapy (CRR) p...

Validation of deep learning-based CT image reconstruction for treatment planning.

Scientific reports
Deep learning-based CT image reconstruction (DLR) is a state-of-the-art method for obtaining CT images. This study aimed to evaluate the usefulness of DLR in radiotherapy. Data were acquired using a large-bore CT system and an electron density phanto...

Infrastructure tools to support an effective Radiation Oncology Learning Health System.

Journal of applied clinical medical physics
PURPOSE: Radiation Oncology Learning Health System (RO-LHS) is a promising approach to improve the quality of care by integrating clinical, dosimetry, treatment delivery, research data in real-time. This paper describes a novel set of tools to suppor...

Unlocking the Power of ChatGPT, Artificial Intelligence, and Large Language Models: Practical Suggestions for Radiation Oncologists.

Practical radiation oncology
Recent advances in artificial intelligence (AI), such as generative AI and large language models (LLMs), have generated significant excitement about the potential of AI to revolutionize our lives, work, and interaction with technology. This article e...