AI in the clinic is judged by the time it gives back to the patient: the lesson from the Medellín symposium

By MiOriente · October 24, 2026.

Sumimedical and Clínica Victoriana held the 2nd Symposium on Innovation in Health and Applied Technology in Medellín. It brought together national and international experts to discuss how artificial intelligence (AI), data analytics and other technologies can improve care and clinical decision-making.

According to MiOriente's report, participants included Colombian physician Héctor Upegui, who has an international track record in industry and AI solutions applied to health and social security. His approach, as quoted by the outlet: “The question is not how much we can automate, but what we do with the time and capabilities that technology gives back to us.”

The discussion boiled down to three ideas: innovating with purpose, enhancing human capabilities (AI supports processes and decisions, and complements professional judgment) and keeping people at the center, without displacing listening, empathy and the bond with the patient. Alejandro García, manager of Clínica Victoriana, added that technology “cannot replace the purpose that drives us, caring for people.”

The agenda featured experts linked to institutions such as Hospital Israelita Albert Einstein (São Paulo), Fundación Santa Fe de Bogotá, Hospital Internacional de Bucaramanga, Universidad de Antioquia, Medisinú, Wicalling Solution, Qualico and ICONTEC. The report offers no figures, concrete use cases or pilot results.

**Our take.** The framework is sensible, but it is a framework, not evidence. “AI gives back time to listen” is a promise that is only kept if someone turns it into a management decision. In general, when a health system automates tasks, the time freed up tends to be absorbed by higher patient volume rather than longer consultations, unless it is set as an explicit goal. Upegui's question is the right one precisely because the answer is not automatic.

That is why metrics should be demanded: consultation minutes per patient, clinicians' administrative burden, errors avoided. Without them, “humanizing with technology” remains a slogan. This applies especially to Latin America, where financial pressure on insurers and institutions is high and the incentive to “do more with the same” is strong.

In the short term, the transition will be uneven: centers with well-organized data and technical capacity will move ahead first, and the rest will fall behind, which could widen gaps in access. In the long term, the underlying thesis is hopeful. AI that supports diagnosis, frees professionals from bureaucracy and brings medicine closer to more people is one of the most plausible paths toward far better health, and toward eradicating diseases that are incurable today. That hospitals talk about purpose before efficiency is a good sign. Now they have to prove it with data.