ReviewBJR open2019
Artificial intelligence in oncology, its scope and future prospects with specific reference to radiation oncology.
Review in BJR open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- The complementary roles of oversight, education, and collaboration in the responsible integration of artificial intelligence in radiation medicine: White paper of CADRA.Journal of applied clinical medical physics · 2026Review
- AI in Radiation Oncology: A Comprehensive Review of Current Applications and Future Directions.Cureus · 2025Review
- Impact of Artificial Intelligence-Based Autosegmentation of Organs at Risk in Low- and Middle-Income Countries.Advances in radiation oncology · 2024Article
- Deep learning-based classification of organs at risk and delineation guideline in pelvic cancer radiation therapy.Journal of applied clinical medical physics · 2023Article
- Excitement and Concerns of Young Radiation Oncologists over Automatic Segmentation: A French Perspective.Cancers · 2023Article
- The influence of artificial intelligence on the work of the medical physicist in radiotherapy practice: a short review.BJR open · 2023Review
- Employing deep convolutional neural networks for segmenting the medial retropharyngeal lymph nodes in CT studies of dogs.Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association · 2022Article
- Machine learning in neuro-oncology: toward novel development fields.Journal of neuro-oncology · 2022Review
- Personalized Automation of Treatment Planning for Linac-Based Stereotactic Body Radiotherapy of Spine Cancer.Frontiers in oncology · 2022Article
- "Evolving role of AI in radiation oncology"- special collection - introductory Editorial.BJR open · 2022Article
- Deep learning-based classification and structure name standardization for organ at risk and target delineations in prostate cancer radiotherapy.Journal of applied clinical medical physics · 2021Article
- Digital Patient-Reported Outcome Measures for Monitoring of Patients on Cancer Treatment: Cross-sectional Questionnaire Study.JMIR formative research · 2021Article
- Artificial intelligence in brachytherapy: a summary of recent developments.The British journal of radiology · 2021Review
- Artificial Intelligence based Models for Screening of Hematologic Malignancies using Cell Population Data.Scientific reports · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveArtificial intelligence (AI) seems to be bridging the gap between the acquisition of data and its meaningful interpretation. These approaches, have shown outstanding capabilities, outperforming most classification and regression methods to date and the ability to automatically learn the most suitable data representation for the task at hand and present it for better correlation. This article tries to sensitize the practising radiation oncologists to understand where the potential role of AI lies and what further can be achieved with it. METHODS AND MATERIALS: Contemporary literature was searched and the available literature was sorted and an attempt at writing a comprehensive non-systematic review was made.
resultsThe article addresses various areas in oncology, especially in the field of radiation oncology, where the work based on AI has been done. Whether it's the screening modalities, or diagnosis or the prognostic assays, AI has come with more accurately defining results and survival of patients. Various steps and protocols in radiation oncology are now using AI-based methods, like in the steps of planning, segmentation and delivery of radiation. Benefit of AI across all the platforms of health sector may lead to a more refined and personalized medicine in near future.
conclusionAI with the use of machine learning and artificial neural networks has come up with faster and more accurate solutions for the problems faced by oncologist. The uses of AI,are likely to get increased exponentially . However, concerns regarding demographic discrepancies in relation to patients, disease and their natural history and reports of manipulation of AI, the ultimate responsibility will rest on the treating physicians.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.