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Artificial Intelligence as a Potential Catalyst to a More Equitable Cancer Care.

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Artificial intelligence (AI) can reduce cancer care inequalities by enhancing diagnostics and access, particularly for underserved populations. Ethical development and implementation are crucial for equitable health care delivery globally.

Keywords:
AIartificial intelligencecancercancer carecatalystchangechangescostcostsdemographicdigital healthepidemiologicalequalityhealth systemhealthcaremHealthmobile healthpublic health

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Area of Science:

  • Digital Health
  • Health Equity
  • Artificial Intelligence in Oncology

Background:

  • Digital interdependence necessitates leveraging artificial intelligence (AI) to transform healthcare.
  • Significant disparities and access barriers persist in healthcare delivery, particularly in cancer care.
  • AI offers potential solutions to mitigate inequalities in health services.

Purpose of the Study:

  • To explore the potential of artificial intelligence (AI) in reducing inequalities within cancer care.
  • To identify key AI applications for improving health equity and access to medical services.
  • To emphasize the role of AI in low- and middle-income countries' health systems.

Main Methods:

  • Viewpoint analysis of AI's role in healthcare transformation and addressing disparities.
  • Identification of AI applications such as health equity monitoring, predictive analytics, and personalized medicine.
  • Discussion of inclusive development practices, ethical considerations, and data representation.

Main Results:

  • AI can improve diagnostic accuracy, optimize resource allocation, and expand access to cancer care.
  • AI-driven tools can enhance health equity monitoring and personalized medicine approaches.
  • Implementation challenges include socioeconomic and geographical disparities, requiring inclusive strategies.

Conclusions:

  • Artificial intelligence (AI) holds significant promise for reducing inequalities in cancer care globally.
  • Inclusive development, ethical considerations, and diverse data are essential for equitable AI deployment.
  • Collaborative efforts are needed to overcome barriers and integrate AI effectively into health systems, with further research on user experiences and socio-cultural factors.