By Teresa Sacchetta
Artificial intelligence is no longer just a promise, but an invisible part of everyday life in healthcare. Algorithms assist in prevention and diagnosis, contribute to the choice of therapies, support image analysis, and optimize hospital workflows. The essential point is to ensure that these advances produce a real impact and are incorporated into existing processes, rather than remaining as sophisticated, unused models. In healthcare, every piece of data produced has the potential to guide a decision that affects lives, increasing the safety of care and ensuring appropriate patient outcomes. Aspects such as waste reduction, optimization of resource use, risk management, increased access for vulnerable populations, and even its use as a complement to medical education can be widely encouraged in this new context.
Brazil is at a decisive moment. The country holds a significant amount of health records and information, supported by a universal public system and a developing innovation ecosystem. At the same time, it faces data fragmentation, which limits the intelligent use of these assets. The Digital Health Strategy for Brazil 20281 and the advancement of standards such as HL7 FHIR2 These represent important steps that need to be assimilated into the culture of healthcare institutions. AI algorithms depend, in order to function well, on complete data, context, diversity, and technical and ethical oversight to serve society.
In this scenario, ethics ceases to be a complement and becomes the very core of innovation. AI systems are only as reliable as the values that guide them. The explainability of the models, replicability, human oversight, and bias mitigation are essential conditions for preserving trust, particularly in the healthcare sector. The Brazilian regulatory debate, materialized in Bill 2338/20233, it aligns with international best practices, such as the European Commission's guidelines for Trustworthy AI.l4 and the World Health Organization's principles on AI for Health5. Technology is evolving at an ever-increasing pace, while trust is built over time, through transparency and accountability in decision-making.
It is still necessary to reduce the gap between prototype and reality. Many AI projects fail to progress due to a lack of integration into workflows or evaluation of clinical outcomes. Technology needs to go beyond efficiency and effectiveness, contributing to a more equitable healthcare system, preventing inequalities or new forms of exclusion. This implies measuring impact on outcomes, quality of life, and access.
The future of digital health will be defined by the capacity for cooperation between institutions, professionals, and citizens. Quality data, solid ethical frameworks, and public policies anchored in societal challenges underpin trust, so that large-scale adoption will come when AI ceases to be news and becomes an invisible tool in a more humane, efficient, effective, and inclusive system.
1 https://www.gov.br/saude/pt-br/acesso-a-informacao/acoes-e-programas/estrategia-de-saude-digital- for-brazil-2028
3 https://www.camara.leg.br/proposicoesWeb/fichadetramitacao?idProposicao=2346646
4 https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai
5 https://www.who.int/publications/i/item/9789240029200
Teresa Sacchetta is the Director of Health at InterSystems and leader of the health business vertical at ABES.
Notice: The opinion presented in this article is the responsibility of its author and not of ABES - Brazilian Association of Software Companies
Article originally published on the Saúde Digital News website: https://saudedigitalnews.com.br/10/12/2025/inteligencia-artificial-na-saude-entre-dados-etica-e-impacto-real/#:~:text=A%20intelig%C3%AAncia%20artificial%20deixa%20de,imagens%20e%20otimizam%20fluxos%20hospitalares
