Evidence map›Paper›PMID 33178922›Full record

ReviewBJR open2019

Artificial intelligence in oncology, its scope and future prospects with specific reference to radiation oncology.

Rajit Rattan, Tejinder Kataria, Susovan Banerjee, Shikha Goyal, Deepak Gupta, Akshi Pandita, Shyam Bisht, Kushal Narang, Saumya Ranjan Mishra

Abstract readReview
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Review
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  7. 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 · 2022
    Article
  8. Review
  9. Article
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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

9 authors.

Rajit RattanDivision of Radiation Oncology, Medanta- The Medicity, Gurgaon, Haryana, India.
Tejinder KatariaDivision of Radiation Oncology, Medanta- The Medicity, Gurgaon, Haryana, India.
Susovan BanerjeeDivision of Radiation Oncology, Medanta- The Medicity, Gurgaon, Haryana, India.
Shikha GoyalDivision of Radiation Oncology, Medanta- The Medicity, Gurgaon, Haryana, India.
Deepak GuptaDivision of Radiation Oncology, Medanta- The Medicity, Gurgaon, Haryana, India.
Akshi PanditaDepartment of Dermatology, P. N. Behl Skin Institute, New Delhi, India.
Shyam BishtDivision of Radiation Oncology, Medanta- The Medicity, Gurgaon, Haryana, India.
Kushal NarangDivision of Radiation Oncology, Medanta- The Medicity, Gurgaon, Haryana, India.
Saumya Ranjan MishraDivision of Radiation Oncology, Medanta- The Medicity, Gurgaon, Haryana, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

PMID33178922
PMCPMC7592433

What OpenQuestion holds

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LicenceCC BY
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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.