AI for physicians: how to use the technology in practice

More than 70% of healthcare professionals already use artificial intelligence to some extent in the hospital, according to the Opinion Box survey conducted in partnership with Rivio in January 2026. The number is surprising until you read the next figure: only 9% work at institutions that have officially adopted AI, and just 13% work at hospitals where it is integrated into management processes.

The technology reached physicians’ daily work long before it reached institutional governance. The result is fragmented, informal use with little structure to unlock the tool’s real potential, or to manage its risks.

This article presents the main AI applications available to physicians today, what Brazilian regulation says about their use and the limits every professional needs to know before adopting these tools.

What AI changes in a physician’s routine

Philips’ 2025 global report on AI in healthcare revealed that 47% of healthcare professionals in Brazil spend more time on administrative tasks than five years ago, and less time with patients. Documentation, filling in medical records, procedure coding, data organization: a growing share of a physician’s working day is taken up by work that does not require clinical judgment.

That is exactly where AI has the most to offer. When it takes over repetitive and cognitively demanding tasks, it frees physicians to focus on what the technology cannot do: judgment in uncertain scenarios, the therapeutic relationship, decisions in the face of ambiguous information and moral responsibility for care.

This model has a name in the literature: augmented intelligence. AI does not replace the physician; it redistributes the physician’s time and attention to where they make the most difference. To understand how this logic applies specifically to clinical diagnosis, read the article AI for medical diagnosis.

The main AI tools for physicians

Automated documentation

This is one of the fastest-adopted applications among physicians. Real-time transcription systems capture the audio of the consultation, identify the relevant clinical elements and automatically fill in the fields of the electronic health record: chief complaint, history, ICD code, management, prescriptions and tests ordered. The physician reviews and validates, without having to type.

The impact is twofold: less administrative time and better-quality records. Consultations documented in real time tend to be more complete and more accurate than those recorded at the end of a long shift.

Clinical decision support

Decision support tools cross-check the patient’s history against up-to-date guidelines, clinical protocols and the scientific literature. The system suggests diagnostic hypotheses, additional investigations and evidence-based management, but it does not decide. Its role is to guide clinical reasoning, especially in atypical cases or in specialties where the physician has less accumulated experience.

This type of tool is particularly useful in primary care, where a single professional has to deal with a wide variety of clinical conditions.

Triage and risk stratification

Triage algorithms classify patients by urgency, identify the risk of clinical deterioration and organize care queues based on objective data. In the emergency room and the ICU, where patient volume and clinical variability are high, this layer of intelligence shortens the response time to critical cases and reduces reliance on subjective assessments for prioritization.

Predictive models applied in this context have already shown significant performance in the early identification of sepsis and arrhythmias, two conditions in which minutes make a direct difference to the clinical outcome.

Remote monitoring and continuity of care

Wearable devices and connected sensors capture clinical data in real time outside the hospital: heart rate, blood pressure, oxygen saturation, sleep patterns and physical activity. This data feeds AI systems that identify changes and generate alerts for the medical team before the patient needs to be admitted.

For chronic patients (people with diabetes, heart disease or COPD), this remote monitoring model changes the logic of follow-up: from periodic appointments to ongoing surveillance, with early intervention when necessary.

Predictive analytics and prevention

Predictive models identify risk patterns before symptoms appear. Probability of hospital readmission, risk of clinical deterioration, likelihood of postoperative complications: information that once existed only in retrospect now guides prospective decisions.

This application turns accumulated clinical data into operational intelligence. The physician acts not only on what they see, but on what the model anticipates.

Governance and regulation of AI use in medicine

In February 2026, the Federal Council of Medicine published CFM Resolution No. 2,454/2026, which regulates the use of artificial intelligence in medicine nationwide. It is the main regulatory reference for physicians and healthcare institutions that adopt or develop AI systems.

The resolution guarantees physicians the right to use AI tools to support clinical decisions, health management, research and medical education, provided the ethical and legal limits of the profession are respected. It also establishes a four-level risk classification for AI systems (low, medium, high and unacceptable), taking into account factors such as the impact on patients’ rights, the system’s degree of autonomy and the sensitivity of the data used.

Institutions that develop or use their own AI systems must set up internal governance processes and, where applicable, create an AI and Telemedicine Committee reporting to the technical directorate. All data used must comply with the LGPD (Brazil’s General Data Protection Law) and health information security standards.

The limits physicians need to know

Adopting AI without understanding its limits is as problematic as not adopting it at all. Three risks deserve special attention.

Algorithmic bias

Systems trained on data that underrepresent certain populations (by race, gender, age group or socioeconomic status) reproduce and amplify these inequalities in clinical results. A model trained mostly on data from European populations may perform significantly worse when assessing Brazilian patients with different genetic or clinical characteristics.

Harmful epistemic dependence

As physicians rely increasingly on the recommendations of AI systems, they may gradually lose the critical capacity to evaluate decisions in real time. Clinicians stop questioning results, especially when they do not understand the algorithm’s internal logic. The risk is not that AI makes mistakes: it is that physicians stop noticing when it does.

Decision opacity

Many AI systems operate as black boxes: they generate recommendations without explaining their reasoning. When the physician does not understand why the system reached a given conclusion, they lose the ability to challenge it on clinical grounds.

Well-applied AI starts with the record

Adopting AI in medical practice has an effect that goes beyond productivity: it improves the quality of care records. When documentation is more complete, the ICD code more accurate and management better recorded, hospital billing better reflects what was actually done.

This chain connects clinical practice to the revenue cycle: more accurate records reduce coding inconsistencies, cut denials for clinical incompatibility and protect the hospital’s revenue.

Rivio works at this layer: using artificial intelligence specialized in the revenue cycle, it identifies inconsistencies between what was recorded and what was charged before the claim reaches the health plan.

Frequently asked questions about AI for physicians

What is AI for physicians?

It is the use of artificial intelligence to support clinical practice: automating documentation, supporting evidence-based decisions, stratifying patient risk, monitoring data in real time and anticipating clinical deterioration. AI works as a layer of support for medical judgment, without replacing it.

Which AI tools are already available to physicians in Brazil?

The main categories available are systems for transcribing and automatically documenting consultations, clinical decision support tools based on guidelines, triage and risk stratification algorithms, remote monitoring platforms for chronic patients and predictive models for clinical deterioration and readmission.

Is the use of AI in medicine regulated in Brazil?

Yes. In February 2026, the CFM published Resolution No. 2,454/2026, which regulates the use of AI in medicine, classifies systems by risk level and sets governance requirements for institutions. Anvisa (National Health Surveillance Agency) complements this regulation by classifying clinical AI systems as software as a medical device (SaMD) under RDC No. 657/2022.

What are the risks of using AI in clinical practice?

The three main ones are algorithmic bias, when the system reproduces inequalities in the data it was trained on; harmful epistemic dependence, when excessive trust in AI erodes the physician’s critical capacity; and decision opacity, when the physician cannot understand or question the algorithm’s logic.

Can AI replace the physician?

The model established in the literature is augmented intelligence: AI amplifies the physician’s capacity, but does not replace clinical judgment, the therapeutic relationship or moral responsibility for care. CFM Resolution No. 2,454/2026 reinforces this principle by guaranteeing physicians the right to use AI as support, while keeping the final clinical decision under their responsibility.