AI for medical diagnosis: how it works and where it is already used

In July 2025, the European Commission’s Joint Research Centre (JRC) published the report AI-driven Innovation in Medical Imaging, which confirmed what part of the medical community had already observed: artificial intelligence systems have reached a high level of maturity in medical image detection and segmentation tasks, with proven impact on clinical practice.

AI in medical diagnosis is changing the logic of care: from medicine that reacts to symptoms to medicine that anticipates risks.

This article explains what AI changes in clinical diagnosis, presents the most established use cases by specialty and shows how this technology operates within the limits of medical responsibility.

What AI changes in clinical diagnosis

AI has brought clinical diagnosis a gain that goes beyond speed: sensitivity. Machine learning and deep learning algorithms identify complex patterns in large volumes of clinical data (tests, histories, biomarkers, images) in ways that exceed unaided human capacity.

A systematic review published in 2025 in the Brazilian Journal of Health Sciences, based on studies from PubMed, Scopus and Web of Science between 2018 and 2025, concluded that AI models perform significantly in the early identification of sepsis, cardiac arrhythmias and cardiovascular diseases, as well as delivering strong results in predicting clinical deterioration, hospital mortality and readmissions.

The concept that describes this scenario is augmented intelligence: AI amplifies medical cognition. The physician remains responsible for the diagnosis and the therapeutic decision. What changes is the quality of the information available at the moment of deciding.

Where AI is already transforming diagnosis, by specialty

Breast cancer screening

There are about 2.3 million breast cancer diagnoses a year worldwide, with approximately 670,000 deaths. As with other cancers, early detection is what most affects the chances of a cure.

AI can predict the risk of the disease up to five years in advance from mammograms with no apparent signs of a tumor. This addresses a long-standing limitation of traditional screening: aggressive, fast-growing tumors, which account for a large share of deaths, are often not visible on conventional exams at an early stage.

The screening model is based on two steps: an initial mammogram followed by AI assessment, which refers only those at high risk for more advanced exams. The result is more accurate, individualized screening.

Radiology and diagnostic imaging

The European Commission study showed that AI systems enable automatic segmentation of multiple organs and the detection and longitudinal follow-up of pulmonary nodules, with measurable gains in diagnostic accuracy and a reduced clinical workload.

Three impacts stand out.

The first is early detection of critical findings: in stroke and brain hemorrhage, automated triage systems flag the exam within seconds, speeding up interventions where minutes make a difference.

The second is less fatigue and human error: AI operates without overload, which is especially useful at peak times or in services with few specialists.

The third is standardized reports: automated interpretation reduces discrepancies between professionals and improves internal audits.

Male infertility screening

In 2024, researchers in Tokyo published in the journal Scientific Reports an AI model capable of predicting the risk of male infertility without semen analysis, which is currently the standard diagnostic method. Developed with data from 3,662 patients, the model achieved 74% accuracy.

The system was designed as an initial screening step before laboratory testing, enabling screening in non-specialized settings. When the AI detects abnormal values, it works as a clinical alert for referral to specialized centers. The model showed high accuracy especially in identifying non-obstructive azoospermia, one of the most severe forms of the condition. The resulting workflow reduces diagnostic delays and allows faster, more targeted treatment.

Oncology and biopsy analysis

In oncology, AI is applied both to analyzing images from blood tests and to reading biopsies. Algorithms identify and count tumor cells with high accuracy, map involved margins, classify complex histological patterns and speed up the release of reports.

The clinical impact is direct: oncologists get faster diagnoses and start personalized therapies sooner. Less time between biopsy and treatment means a better chance of success. This shift brings oncology closer to the concept of preventive medicine, with diagnosis in the preclinical phase, a higher cure rate and fewer aggressive treatments.

AI in diagnosis and Brazilian regulation

The advance of diagnostic AI in Brazil has a regulatory framework. Anvisa (National Health Surveillance Agency), through RDC No. 657/2022, classifies systems based on artificial intelligence as medical software (SaMD — Software as a Medical Device). This means AI tools used in clinical diagnosis are subject to validation, traceability and safety requirements before they are adopted in care.

This regulation has two implications for hospitals and clinics. The first concerns compliance: adopting an AI system without checking its regulatory classification exposes the institution to legal and clinical risks. The second concerns responsibility: the final clinical decision remains with the physician. AI supports, stratifies and alerts, but it does not decide.

The physician’s role in the era of assisted diagnosis

The augmented intelligence model clearly defines the role of each party: AI processes, correlates and alerts. The physician interprets, decides and is accountable.

This matters for two practical reasons. The first is the risk of algorithmic hallucination, when the system generates plausible conclusions with no real basis in the data or the scientific literature. Without human oversight, this risk translates into clinical errors with real consequences for the patient. The second is the relational dimension of diagnosis: family context, life history and the nuances of physician-patient communication are dimensions that current AI does not capture.

Physicians who understand where AI helps and where it falls short use the technology more efficiently and safely. For a complete overview of the AI tools available for day-to-day clinical work, read the article AI for physicians: how to use the technology in practice.

From diagnostic precision to record quality

More precise diagnosis has an effect that goes beyond the clinical outcome: it improves the quality of the care record. When AI correctly identifies a condition at an early stage, the physician documents it more precisely, the ICD code is recorded more accurately and billing better reflects what was actually done.

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

Rivio uses artificial intelligence specialized in the revenue cycle and identifies inconsistencies between what was recorded and what was charged before the claim reaches the health plan.

Frequently asked questions about AI for medical diagnosis

What is AI for medical diagnosis?

It is the use of artificial intelligence algorithms to identify patterns in clinical data, medical images and biomarkers, with the goal of supporting diagnosis, anticipating risks and guiding therapeutic decisions. AI amplifies the diagnostic sensitivity of the physician, who remains responsible for the clinical decision.

Can AI replace the physician in diagnosis?

No. The model established in the literature is augmented intelligence: AI amplifies diagnostic capacity, but responsibility for the diagnosis and the clinical decision remains with the physician. Brazilian regulation (Anvisa RDC No. 657/2022) reinforces this logic by classifying AI systems as medical software subject to validation and human oversight.

In which specialties is AI already used in diagnosis?

The most established applications are in radiology and diagnostic imaging, oncology (breast cancer screening and biopsy analysis), cardiology (classification of cardiovascular diseases and arrhythmias) and reproductive medicine (male infertility screening). The list grows as new models are validated.

How does AI improve the early diagnosis of diseases?

AI identifies patterns in large volumes of clinical data that escape unaided human perception. In breast cancer screening, it can predict risk up to five years in advance from mammograms with no visible changes. In hospitals, predictive models identify the risk of clinical deterioration before signs appear in traditional assessment.