Evidence map›Paper›PMID 33914614›Full record

ReviewThe British journal of radiology2021

Artificial intelligence in brachytherapy: a summary of recent developments.

Susovan Banerjee, Shikha Goyal, Saumyaranjan Mishra, Deepak Gupta, Shyam Singh Bisht, Venketesan K, Kushal Narang, Tejinder Kataria

Abstract readReview
In one paragraph

Review in The British journal of radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Systematic review of artificial intelligence in brachytherapy.Journal of contemporary brachytherapy · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Susovan BanerjeeDivision of Radiation Oncology, Medanta- The Medicity, Gurgaon, Haryana, India.
Shikha GoyalDepartment of Radiotherapy, Post Graduate Institute of Medical Education and Research, Chandigarh, India.
Saumyaranjan MishraDivision of Radiation Oncology, Medanta- The Medicity, Gurgaon, Haryana, India.
Deepak GuptaDivision of Radiation Oncology, Medanta- The Medicity, Gurgaon, Haryana, India.
Shyam Singh BishtDivision of Radiation Oncology, Medanta- The Medicity, Gurgaon, Haryana, India.
Venketesan KDivision of Radiation Oncology, Medanta- The Medicity, Gurgaon, Haryana, India.
Kushal NarangDivision of Radiation Oncology, Medanta- The Medicity, Gurgaon, Haryana, India.
Tejinder KatariaDivision of Radiation Oncology, Medanta- The Medicity, Gurgaon, Haryana, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) applications, in the form of machine learning and deep learning, are being incorporated into practice in various aspects of medicine, including radiation oncology. Ample evidence from recent publications explores its utility and future use in external beam radiotherapy. However, the discussion on its role in brachytherapy is sparse. This article summarizes available current literature and discusses potential uses of AI in brachytherapy, including future directions. AI has been applied for brachytherapy procedures during almost all steps, starting from decision-making till treatment completion. AI use has led to improvement in efficiency and accuracy by reducing the human errors and saving time in certain aspects. Apart from direct use in brachytherapy, AI also contributes to contemporary advancements in radiology and associated sciences that can affect brachytherapy decisions and treatment. There is a renewal of interest in brachytherapy as a technique in recent years, contributed largely by the understanding that contemporary advances such as intensity modulated radiotherapy and stereotactic external beam radiotherapy cannot match the geometric gains and conformality of brachytherapy, and the integrated efforts of international brachytherapy societies to promote brachytherapy training and awareness. Use of AI technologies may consolidate it further by reducing human effort and time. Prospective validation over larger studies and incorporation of AI technologies for a larger patient population would help improve the efficiency and acceptance of brachytherapy. The enthusiasm favoring AI needs to be balanced against the short duration and quantum of experience with AI in limited patient subsets, need for constant learning and re-learning to train the AI algorithms, and the inevitability of humans having to take responsibility for the correctness and safety of treatments.

Indexed as

Artificial IntelligenceBrachytherapyForecastingHumansMedical ErrorsRadiation Oncology

Identifiers

PMID33914614
PMCPMC8173686

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.