Artificial intelligence in healthcare: a hospital revolution

Artificial intelligence in healthcare is already part of the routine at hospitals, clinics and billing teams, but still in a fragmented way. A survey by Instituto Opinion Box, in partnership with Rivio, conducted in January 2026 with 349 professionals in the industry, reveals the paradox of the moment: more than 70% of professionals say they use AI to some extent, but only 9% work at institutions that have officially adopted the technology, and only 13% work at hospitals where AI is integrated into management processes.

The interest is there: 80% of the professionals surveyed say they want to use AI officially at work. What is missing, according to the data, is not access to the technology but the structure to integrate it into institutions’ critical processes.

What artificial intelligence does in healthcare

More than a technology in use, AI in healthcare mobilizes a set of data and systems to solve specific problems. The main fronts of use, according to the Opinion Box/Rivio survey, are frontline activities (scheduling, monitoring, 24-hour service and queue management), adopted by 47% of the institutions that already use the technology.

Management activities appear in only 32% of cases. This underuse in strategic routines and in the revenue cycle shows there is still a great deal of potential in using AI as a financial solution for hospitals. Learn about other uses of AI in the healthcare industry.

Clinical diagnosis and medical imaging

Algorithms trained on large volumes of images identify patterns in X-rays, CT scans and MRIs with a speed and consistency that are hard to achieve in continuous manual analysis.

Clinical decision support

Systems integrated with electronic medical records analyze clinical records, patient history and lab data to suggest differential diagnoses, alert to drug interactions and support the definition of treatment protocols. In oncology, algorithms are used to adjust radiotherapy and chemotherapy protocols based on the specific biological characteristics of the tumor.

Administrative automation and billing

On the operational side, AI automates repetitive tasks such as extracting data from medical records, coding diagnoses, transcribing documents and validating medical claims. Systems that cross-check clinical records against billed items identify inconsistencies before claims are sent to payers, reducing denials and recovering revenue that would be lost in manual review.

Triage and access

Chatbots and virtual assistants help patients identify symptoms, schedule appointments and be directed to the right level of care, which is especially relevant in regions with a shortage of health professionals.

Where AI adoption stands in Brazilian healthcare

Two complementary surveys outline the current landscape of AI adoption in Brazilian healthcare.

The Opinion Box/Rivio survey shows that, despite high individual use, institutionalization is low:

• More than 70% of professionals use AI to some extent, but in an occasional or experimental way.

• Only 9% work at institutions that have officially adopted the technology.

• Only 13% work at hospitals where AI is integrated into management processes.

• 80% are interested in using AI officially at work.

The Hospital Digital Maturity Map from Anahp (National Association of Private Hospitals) reinforces the structural diagnosis: Brazilian hospitals score 49% in digital maturity, indicating that technology is advancing faster than institutions’ ability to prepare teams and processes to absorb it.

AI applied to the hospital revenue cycle: the biggest gap

The most critical finding of the Opinion Box/Rivio survey for hospital financial management is this: only 17% of institutions use AI in the revenue cycle, precisely the process most directly tied to the hospital’s financial sustainability.

The impact of this gap has a known size. According to Anahp, R$ 5.8 billion in hospital revenue is withheld by payers every year, on the grounds of administrative errors, communication failures, improperly completed records and inconsistencies in procedure coding. These are the problems Rivio’s AI was built to solve.

AI acts at three critical points in this cycle:

Concurrent and retrospective audit

AI agents read clinical records (including prescriptions, nursing progress notes and reports) and cross-check that information against the items billed on the claim. Discrepancies such as unbilled procedures, incompatible codes or amounts outside the contract are identified during or right after care, before they lead to a denial from the payer.

Billing and submission

Automatic validation of XML files before submission reduces coding errors and incompatibilities with each payer’s contractual rules. The process eliminates manual bottlenecks and shortens the time between care and billing.

Denial and appeals management

Once payers respond, AI classifies the denials received, cross-checks each item against the original claim and builds the grounds for the appeal, citing the clinical and contractual records that support the charge. What used to take hours of manual work is now processed in minutes, covering 100% of claims.

What AI changes for health professionals

When AI takes on repetitive, low-judgment tasks, such as filling in reports, reviewing literature and processing documents, professionals gain time for what requires human capacity: the relationship with the patient, listening, ethical decisions and clinical judgment in ambiguous situations.

The high interest professionals report (80% want to use AI officially) signals that internal demand already exists in institutions. The next step is to turn that interest into structure: governance, training, integration with ERPs and clearly defined management processes.

Rivio was founded to transform hospital management through artificial intelligence. In a landscape under growing pressure from costs, regulatory complexity and operational inefficiencies, we believe technology is the way to bring financial predictability, scale and intelligence back to healthcare’s administrative processes.

Our vision is clear: to build the best operating system for healthcare in Latin America, starting with the hospital revenue cycle. By automating analysis, reducing rework and supporting decisions with reliable data, we help hospitals operate more efficiently, free up their teams’ time and create the conditions to focus on what really matters: quality of care and the patient experience.

Frequently asked questions about AI in healthcare

Can AI replace physicians and nurses?

No. AI in healthcare works as a support tool: it expands diagnostic capacity, automates administrative tasks and speeds up processes, but it does not replace clinical judgment, the relationship with the patient or ethical decisions.

How many healthcare professionals already use AI in Brazil?

More than 70%, according to the Opinion Box/Rivio survey (January 2026). However, that use is still mostly occasional and experimental. The TIC Saúde 2024 survey (Cetic.br) confirms it: 17% of physicians and 16% of nurses use AI in their individual routine.

Why have only 9% of institutions officially adopted AI?

The main barrier is not cost (cited by only 12% of respondents in the Opinion Box/Rivio survey), but the lack of an organizational culture (20%), unfamiliarity with the tools (18%) and lack of team training (15%). The issue is governance and training, not budget.

How does AI reduce hospital denials?

AI systems cross-check the clinical records in the medical record against the items billed on the claim. Inconsistencies (such as unbilled procedures, incompatible codes or amounts outside the contract) are identified before submission to the payer. The Opinion Box/Rivio survey shows that only 17% of institutions already use AI in billing, which means most still face avoidable denials.

Does AI need to integrate with the hospital’s ERP?

Yes. To operate in the revenue cycle, AI needs to integrate with the hospital management system to access clinical and financial records. The main platforms already offer connectors for the most widely used ERPs in Brazil: Tasy, MV, Pixeon, Wareline and SPDATA.

Is it safe to use AI with patient data?

Yes, as long as the platform operates in compliance with the LGPD (Brazil’s General Data Protection Law), with data encryption, access control and traceability of operations. Serious vendors provide technical documentation on security and regulatory compliance.

How do you assess whether a healthcare AI vendor is reliable?

The main criteria are: a proven track record at healthcare institutions, compliance with the LGPD and ANVISA (Brazil’s National Health Surveillance Agency) regulations, transparency in its algorithms, integration with the hospital’s ERP, verifiable references and a training plan for teams. Vendors that offer a contractual performance guarantee are the safest option for the revenue cycle.