AI for Precision Oncology
Artificial intelligence is rapidly transforming cancer research by enabling more personalized and data-driven treatment decisions. Recent media coverage and outreach highlight the work of María Jesús Ledesma Carbayo, member of the Information Processing and Telecommunications Center (IPTC) at Universidad Politécnica de Madrid (UPM), whose research focuses on developing AI tools to predict which cancer patients are most likely to benefit from immunotherapy.
Her multidisciplinary team has developed an algorithms that combine medical imaging, clinical information and blood test data to identify patients with non-small cell lung cancer who are more likely to respond to immunotherapy. Since only around 20–30% of patients currently benefit from these treatments, improving patient selection could optimize therapeutic strategies, reduce unnecessary side effects and enhance healthcare efficiency.
Originally supported by a Fundación BBVA Leonardo Grant, the research is now expanding beyond lung cancer to other challenging diseases, including hematological malignancies and triple-negative breast cancer. The project is carried out in close collaboration with leading Spanish hospitals and research institutions, illustrating the importance of interdisciplinary partnerships in translating AI innovations into clinical practice.
As highlighted in media reports and María Jesús Ledesma Carbayo’s LinkedIn post, artificial intelligence is becoming an essential tool for decoding the biology of cancer, supporting clinicians in making more informed decisions and accelerating the implementation of precision medicine. These predictive models are expected to begin supporting routine clinical decision-making within the next three to five years, bringing more personalized cancer care closer to patients.
María Jesús Ledesma Carbayo: GS / ORCID / LinkedIn
Source of the image: article of the “El Mundo”; author: Sergio González Valero.
Article based on articles from:
El Mundo (20.06.2026): https://short.upm.es/qow4w
Diario Médico: https://short.upm.es/4sfjh
LinkedIn: https://www.linkedin.com/company/iptc-upm/
For more information: www.iptc.upm.es
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