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Radiation: Applications01:17

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The average temperature of Earth is the subject of much current discussion. Earth is in radiative contact with both the Sun and dark space; it receives almost all its energy from the radiation of the Sun and reflects some of it into outer space. Dark space is very cold, about 3 K, so Earth radiates energy into it. For instance, heat transfer occurs from soil and grasses, the rate of which can be so rapid that frost can occur on clear summer evenings, even in warm latitudes.
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[Artificial intelligence applied to radiation oncology].

J-E Bibault1, A Burgun2, P Giraud1

  • 1Service d'oncologie radiothérapie, hôpital européen Georges-Pompidou, 20, rue Leblanc, 75015 Paris, France; Université Paris Descartes, Paris Sorbonne Cité, 20, rue Leblanc, 75015 Paris, France.

Cancer Radiotherapie : Journal De La Societe Francaise De Radiotherapie Oncologique
|April 24, 2017
PubMed
Summary
This summary is machine-generated.

Mining large patient databases and electronic health records using AI offers a new way to personalize radiation oncology treatments. This approach addresses challenges in conducting clinical trials for new therapies like intensity-modulated radiation therapy.

Keywords:
Apprentissage automatiséArtificial intelligenceIntelligence artificielleMachine learningModèle prédictifPredictive modelRadiation oncologyRadiothérapie

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Area of Science:

  • Radiation Oncology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Conducting randomized clinical trials for new radiation oncology treatments, such as intensity-modulated radiation therapy, is challenging.
  • Existing national databases often lack detailed patient and treatment data.
  • Electronic health records offer detailed patient phenotypic profiles.

Purpose of the Study:

  • To review the emerging paradigm of mining large databases for clinical and translational research in radiation oncology.
  • To explore the use of artificial intelligence and machine learning for personalized treatment strategies.
  • To discuss how these data mining methods have been applied in previous studies.

Main Methods:

  • Utilizing electronic health records for detailed patient phenotyping.
  • Leveraging Record-and-Verify systems for precise treatment documentation.
  • Applying artificial intelligence and machine learning algorithms to analyze large datasets.

Main Results:

  • This review synthesizes existing studies demonstrating the application of data mining and AI in radiation oncology.
  • Identifies the potential of these methods to generate hypotheses for treatment personalization.
  • Highlights the value of detailed patient and treatment data for research.

Conclusions:

  • Data mining of electronic health records and treatment data, powered by AI, presents a novel approach to radiation oncology research.
  • This paradigm shift can overcome limitations of traditional clinical trials.
  • It facilitates the generation of evidence for personalized cancer treatments.