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Aims: Artificial intelligence has the potential to transform the radiotherapy workflow, resulting in improved quality, safety, accuracy and timeliness of radiotherapy delivery. Several commercially available artificial intelligence-based auto-contouring tools have emerged in recent years. Their clinical deployment raises important considerations for clinical oncologists, including quality assurance and validation, education, training and job planning. Despite this, there is little in the literature capturing the views of clinical oncologists with respect to these factors.
Materials And Methods: The Royal College of Radiologists realises the transformational impact artificial intelligence is set to have on our specialty and has appointed the Artificial Intelligence for Clinical Oncology working group. The aim of this work was to survey clinical oncologists with regards to perceptions, current use of and barriers to using artificial intelligence-based auto-contouring for radiotherapy. Here we share our findings with the wider clinical and radiation oncology communities. We hope to use these insights in developing support, guidance and educational resources for the deployment of auto-contouring for clinical use, to help develop the case for wider access to artificial intelligence-based auto-contouring across the UK and to share practice from early-adopters.
Results: In total, 78% of clinical oncologists surveyed felt that artificial intelligence would have a positive impact on radiotherapy. Attitudes to risk were more varied, but 49% felt that artificial intelligence will decrease risk for patients. There is a marked appetite for urgent guidance, education and training on the safe use of such tools in clinical practice. Furthermore, there is a concern that the adoption and implementation of such tools is not equitable, which risks exacerbating existing inequalities across the country.
Conclusion: Careful coordination is required to ensure that all radiotherapy departments, and the patients they serve, may enjoy the benefits of artificial intelligence in radiotherapy. Professional organisations, such as the Royal College of Radiologists, have a key role to play in delivering this.
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http://dx.doi.org/10.1016/j.clon.2023.01.014 | DOI Listing |
JMIR Res Protoc
September 2025
University of Nevada, Las Vegas, Las Vegas, NV, United States.
Background: In-hospital cardiac arrest (IHCA) remains a public health conundrum with high morbidity and mortality rates. While early identification of high-risk patients could enable preventive interventions and improve survival, evidence on the effectiveness of current prediction methods remains inconclusive. Limited research exists on patients' prearrest pathophysiological status and predictive and prognostic factors of IHCA, highlighting the need for a comprehensive synthesis of predictive methodologies.
View Article and Find Full Text PDFJ Med Internet Res
September 2025
Department of Community Medicine, Faculty of Health, UiT The Arctic University of Norway, Tromsø, Norway.
Background: The ability to access and evaluate online health information is essential for young adults to manage their physical and mental well-being. With the growing integration of the internet, mobile technology, and social media, young adults (aged 18-30 years) are increasingly turning to digital platforms for health-related content. Despite this trend, there remains a lack of systematic insights into their specific behaviors, preferences, and needs when seeking health information online.
View Article and Find Full Text PDFEmerg Top Life Sci
September 2025
Hurdle.bio / Chronomics Ltd., London, UK.
Artificial intelligence (AI) is transforming many fields, including healthcare and medicine. In biomarker discovery, AI algorithms have had a profound impact, thanks to their ability to derive insights from complex high-dimensional datasets and integrate multi-modal datatypes (such as omics, electronic health records, imaging or sensor and wearable data). However, despite the proliferation of AI-powered biomarkers, significant hurdles still remain in translating them to the clinic and driving adoption, including lack of population diversity, difficulties accessing harmonised data, costly and time-consuming clinical studies, evolving AI regulatory frameworks and absence of scalable diagnostic infrastructure.
View Article and Find Full Text PDFRetina
September 2025
Department of Ophthalmology, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 15, CH-3010.
Purpose: To evaluate inter-grader variability in posterior vitreous detachment (PVD) classification in patients with epiretinal membrane (ERM) and macular hole (MH) on spectral-domain optical coherence tomography (SD-OCT) and identify challenges in defining a reliable ground truth for artificial intelligence (AI)-based tools.
Methods: A total of 437 horizontal SD-OCT B-scans were retrospectively selected and independently annotated by six experienced ophthalmologists adopting four categories: 'full PVD', 'partial PVD', 'no PVD', and 'ungradable'. Inter-grader agreement was assessed using pairwise Cohen's kappa scores.
Cuad Bioet
September 2025
Universidad de A Coruña. Facultad de Derecho, Campus de Elviña, s/n, 15071, A Coruña. 981 167000 ext. 1640
The implications of the use of artificial intelligence (AI) in many areas of human existence compels us to reflect on its ethical relevance. This paper addresses the signification of its use in healthcare for patient informed consent. To this end, it first proposes an understanding of AI, as well as the basis for informed consent.
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