Keywords
inteligencia artificial, justicia, salud global, equidad, datos de salud, bioética, artificial intelligence, justice, global health, equity, health data, bioethics
El papel de la IA en la universalización de la salud: ¿una cuestión de justicia?
Abstract
El texto analiza si la inteligencia artificial (IA) puede contribuir de manera justa a la universalización del derecho a la salud, especialmente en contextos donde la atención médica es limitada por razones económicas o geográficas. Tras identificar las principales aplicaciones de la IA en diagnóstico, terapias personalizadas, medicina preventiva, investigación biomédica y gestión hospitalaria, el autor subraya que estas tecnologías podrían mejorar el acceso, reducir costes y acelerar la innovación sanitaria. Sin embargo, la dimensión ética introduce dudas relevantes. El documento traza un “mapa de injusticias” que incluye cuatro riesgos principales: la pobreza de datos que excluye estructuralmente a poblaciones vulnerables; la injusticia procedimental derivada de algoritmos opacos que reproducen desigualdades previas; la exclusión de prioridades de investigación que favorece intereses económicos o geopolíticos; y la falta de equidad en el reparto de beneficios, que deja fuera a quienes contribuyen con datos o participación. Para evaluar estas injusticias, el texto compara tres marcos teóricos de la justicia —libertario, rawlsiano y comunitarista— mostrando cómo cada enfoque redefine lo que puede considerarse justo. Finalmente, la obra invita a repensar la universalización de la salud más allá de la eficiencia tecnológica, situando la equidad, la participación y el bien común como criterios centrales.
The text examines whether artificial intelligence (AI) can contribute fairly to the universalisation of the right to health, particularly in regions where medical care is limited by economic or geographical constraints. After outlining key applications of AI—medical diagnosis, personalised treatments, preventive medicine, biomedical research, and hospital management—the author highlights its potential to expand access, reduce costs, and accelerate innovation. Yet the ethical dimension raises significant concerns. The document maps four core injustices: data poverty, which structurally excludes vulnerable populations; procedural injustice caused by opaque algorithms that reproduce pre‑existing inequalities; exclusion in research priorities driven by economic or geopolitical interests; and inequity in the distribution of benefits, where communities providing data or participation do not share in the outcomes. To assess these injustices, the text compares three theoretical models of justice—libertarian, Rawlsian, and communitarian—showing how each framework reshapes the notion of fairness. Ultimately, the work argues that the universalisation of health cannot rely solely on technological efficiency: it requires ensuring representation, transparency, equitable research agendas, and shared benefits. AI is presented as a promising but ethically complex tool whose contribution to global health will depend on adopting justice‑oriented governance.
The text examines whether artificial intelligence (AI) can contribute fairly to the universalisation of the right to health, particularly in regions where medical care is limited by economic or geographical constraints. After outlining key applications of AI—medical diagnosis, personalised treatments, preventive medicine, biomedical research, and hospital management—the author highlights its potential to expand access, reduce costs, and accelerate innovation. Yet the ethical dimension raises significant concerns. The document maps four core injustices: data poverty, which structurally excludes vulnerable populations; procedural injustice caused by opaque algorithms that reproduce pre‑existing inequalities; exclusion in research priorities driven by economic or geopolitical interests; and inequity in the distribution of benefits, where communities providing data or participation do not share in the outcomes. To assess these injustices, the text compares three theoretical models of justice—libertarian, Rawlsian, and communitarian—showing how each framework reshapes the notion of fairness. Ultimately, the work argues that the universalisation of health cannot rely solely on technological efficiency: it requires ensuring representation, transparency, equitable research agendas, and shared benefits. AI is presented as a promising but ethically complex tool whose contribution to global health will depend on adopting justice‑oriented governance.
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