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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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Making radiotherapy more efficient with FAIR data.

Petros Kalendralis1, Matthijs Sloep1, Johan van Soest1

  • 1Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands.

Physica Medica : PM : an International Journal Devoted to the Applications of Physics to Medicine and Biology : Official Journal of the Italian Association of Biomedical Physics (AIFB)
|February 27, 2021
PubMed
Summary
This summary is machine-generated.

This article highlights the importance of FAIR data principles for artificial intelligence in radiotherapy. It explains how making data findable, accessible, interoperable, and reusable is crucial for advancing data-driven healthcare in this field.

Keywords:
Artificial intelligenceFAIR dataRadiotherapy

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Area of Science:

  • Medical Physics
  • Radiotherapy
  • Artificial Intelligence
  • Data Science

Background:

  • Artificial intelligence (AI) is rapidly expanding within radiotherapy.
  • Healthcare is increasingly shifting towards data-driven approaches.
  • The FAIR data principles are essential for managing this data transformation.

Purpose of the Study:

  • To summarize the need and application of FAIR data principles in radiotherapy.
  • To introduce the FAIR data concept and its relevance.
  • To provide guidance on FAIR data implementation for radiotherapy stakeholders.

Main Methods:

  • Review of AI applications in radiotherapy.
  • Explanation of FAIR (Findable, Accessible, Interoperable, Reusable) data principles.
  • Identification of use cases and stakeholder roles.

Main Results:

  • FAIR data principles are critical for effective AI implementation in radiotherapy.
  • Practical use cases demonstrate the value of FAIR data.
  • Medical physicists play a central role in adopting FAIR data.

Conclusions:

  • Adopting FAIR data principles is vital for advancing AI in radiotherapy.
  • Clear guidance and defined roles are needed for successful implementation.
  • This approach supports the transition to data-driven healthcare in oncology.