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Artificial Intelligence as a Potential Catalyst to a More Equitable Cancer Care.
Garcia-Saiso, Sebastian; Marti, Myrna; Pesce, Karina; Luciani, Silvana; Mujica, Oscar; Hennis, Anselm; D'Agostino, Marcelo.
Affiliation
  • Garcia-Saiso S; Pan American Health Organization, Washington, DC, United States.
  • Marti M; Pan American Health Organization, Washington, DC, United States.
  • Pesce K; Hospital Italiano de Buenos Aires, Buenos Aires, Argentina.
  • Luciani S; Pan American Health Organization, Washington, DC, United States.
  • Mujica O; Pan American Health Organization, Washington, DC, United States.
  • Hennis A; Pan American Health Organization, Washington, DC, United States.
  • D'Agostino M; Pan American Health Organization, Washington, DC, United States.
JMIR Cancer ; 10: e57276, 2024 Aug 12.
Article in En | MEDLINE | ID: mdl-39133537
ABSTRACT
As we enter the era of digital interdependence, artificial intelligence (AI) emerges as a key instrument to transform health care and address disparities and barriers in access to services. This viewpoint explores AI's potential to reduce inequalities in cancer care by improving diagnostic accuracy, optimizing resource allocation, and expanding access to medical care, especially in underserved communities. Despite persistent barriers, such as socioeconomic and geographical disparities, AI can significantly improve health care delivery. Key applications include AI-driven health equity monitoring, predictive analytics, mental health support, and personalized medicine. This viewpoint highlights the need for inclusive development practices and ethical considerations to ensure diverse data representation and equitable access. Emphasizing the role of AI in cancer care, especially in low- and middle-income countries, we underscore the importance of collaborative and multidisciplinary efforts to integrate AI effectively and ethically into health systems. This call to action highlights the need for further research on user experiences and the unique social, cultural, and political barriers to AI implementation in cancer care.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: JMIR Cancer Year: 2024 Document type: Article Affiliation country: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: JMIR Cancer Year: 2024 Document type: Article Affiliation country: United States