37P Fix the data, not the model: LMIC challenges in AI for precision oncology

H. Parupalli; R. Digumarti; K.C. Puligundla; R. Parimkayala; Vishal Toka

doi:10.1016/j.esmoop.2026.106141

April 1, 2026ESMO OpenOpen Access

37P Fix the data, not the model: LMIC challenges in AI for precision oncology

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Key Points

The research aims to address the challenges in data quality and its impact on precision oncology trials in low- and middle-income countries (LMICs).
Assessment of existing data quality issues in LMICs
Evaluation of the impact of reclassification on patient eligibility
Analysis of genotype-guided trial processes
Improved data reclassification increases patient inclusion in trials
Enhanced access to DDR-targeting therapies for more patients in LMICs
Identification of critical gaps in current data management practices

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Implication

Analysis reveals the need to improve data inclusion for precision oncology trials in LMICs, suggesting better therapy access.

Abstract

reclassification provides a rationale to include these patients in genotype-guided precision oncology trials, potentially expanding eligibility for DDR-targeting therapies.

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Parupalli et al. (2026) studied this question.

synapsesocial.com/papers/69d0adc2659487ece0fa4405 https://doi.org/10.1016/j.esmoop.2026.106141

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37P Fix the data, not the model: LMIC challenges in AI for precision oncology

Key Points

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Implication

Abstract

Discussion

Cite This Study

37P Fix the data, not the model: LMIC challenges in AI for precision oncology

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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