Government programs in Brazil's October 2026 presidential race, from Luiz Inácio Lula da Silva of the Workers' Party (PT) to Flávio Bolsonaro of the Liberal Party (PL), promise to use artificial intelligence in the SUS, the country's universal public health system, to order queues for appointments, exams and surgery by clinical risk and to support diagnoses. A joint report by g1 and BBC News Brasil shows the plans run into practical obstacles: the system handles about 2.8 billion patient visits a year, there is no consolidated national count of how many people are waiting in line, and much of the available data was created for billing rather than clinical use.
What the plans promise
The proposals are similar in essence and, according to the report, light on detail. Flávio Bolsonaro's program promises AI to speed up the scheduling of appointments and exams and to support prevention: "The system will be able to identify who runs a greater risk of falling ill and call that person in to get care in time, instead of waiting for them to arrive at the emergency room when it is already serious." Lula's plan calls for improving the "regulation of access to specialized care", with more transparency in queues and AI "with a single digital queue ordered by clinical risk". Renan Santos of the Missão party proposes replacing the chronological queue and cites the DoctorSV platform created by the government of El Salvador.
What is settled and what is still uncertain
Among researchers, AI in health care is seen as a likely path. "The factors that lead a person to have a serious health problem, or even to die, are usually complex interactions, and that is exactly what these algorithms do so well," said Alexandre Chiavegatto Filho, a professor of artificial intelligence at the University of São Paulo's School of Public Health who coordinates its Big Data and Predictive Analysis Laboratory. One use he finds promising is extending access to specialists in regions where they are scarce; his laboratory develops algorithms trained on data from Brazilian patients.
The central application of the campaign promises, reordering queues, is more uncertain. Wagner Meira Jr., a professor in the computer science department of the Federal University of Minas Gerais (UFMG) and a researcher at its Center for Innovation in Artificial Intelligence for Health, called some of the proposals futuristic. "They are hardly achieved within four years of government," he said. In his assessment, AI already performs better in contained, well-defined tasks such as analyzing exams: the more delimited the task, the greater the success. He also points to an obvious limit, prioritizing patients does not create new appointment slots.
"To improve the queue, before anything else you have to increase the number of people serving the queue. Then you think about how to prioritize it. If you simply change people's positions and keep the same capacity constraint, it will not improve much." (Wagner Meira Jr., UFMG)
Another obstacle is the data itself. According to Meira Jr., much of the information in the SUS consists of administrative records created to pay service providers. "These data are very rich. But they are very limited for this whole range of scenarios," he said. The Health Ministry confirmed to the reporters that there is still no consolidated national figure for people waiting in the SUS, because queues for surgeries, appointments and exams are managed by states and municipalities, which until recently were not required to send that information to the federal government. A 2025 ministerial order now requires periodic, standardized submission of those records to the National Health Data Network (RNDS), but the measure is still being implemented. The only source available today is Sisreg, a scheduling and regulation system that not all municipalities use.
For readers, two practical points. First, the queue you are waiting in is managed by your state and your city, not Brasília; questions about your place in line for appointments, exams or surgery should go to the municipal or state health department. Second, when judging promises of AI in health care, check whether they come with measures to expand capacity, such as hiring staff and adding services, and with investment in quality clinical data. According to the researchers interviewed, without that the only thing that changes is the order of the line.