AI in Genomics: Sequencing 40% Faster by 2026

A study published in the journal Nature Medicine (2026) revealed that AI systems accelerate genomic sequencing by 40%, enabling the identification of genetic variants associated with rare diseases in hours, rather than weeks. Hospital Israelita Albert Einstein, which participated in the study, has already implemented the technology to support geneticists. The results are impressive, but can algorithmic precision replace human clinical judgment?

Global investment in AI for genomics is expected to reach US$12 billion in 2026 (MarketsandMarkets, 2026). Meanwhile, a Datafolha survey (2026) reveals a paradox: 68% of Brazilians trust AI-based genetic diagnoses, but 65% prefer having a human physician responsible for their treatment. Technology is advancing faster than regulation and the ethical debate.

The new clinical eye: how AI reads the genome

Deep learning algorithms have transformed genomics. Systems trained on millions of genetic sequences now identify mutations, variants, and predispositions with superhuman accuracy. Companies such as Google Health, Illumina, and the Brazilian firm Genomika dominate this market, providing platforms that analyze genomic data in minutes.

Hospital Israelita Albert Einstein is the leading Brazilian example. The 30 AI systems installed process around 500 sequencing runs per month, prioritizing urgent cases and flagging potential anomalies (Hospital Israelita Albert Einstein, 2026). When the algorithm detects a suspicious variant, the geneticist is alerted for immediate review. Wait times for reports have dropped from 30 days to 5 days.

The technology has also reached precision oncology. Algorithms analyze liquid biopsies to identify cancer-causing mutations with 95% accuracy (Nature Medicine, 2026). A.C. Camargo Hospital, a reference in oncology, has adopted the technology for biopsy screening, allowing pathologists to focus on the most complex cases.

The price of precision: privacy and unequal access

The effectiveness numbers are hard to ignore. But critics point to a cost that does not appear in clinical statistics. When an algorithm gets a genetic diagnosis wrong, who is responsible? The physician who relied on the system, the hospital that implemented it, or the company that developed it? Brazilian legislation does not yet answer this question.

There is also the problem of unequal access. AI systems are expensive and require technological infrastructure that many public hospitals lack. While Hospital Israelita Albert Einstein processes 500 sequencing runs per month, healthcare units in the North and Northeast regions still rely on manual reports that take months. Technology could widen the gap between elite medicine and public medicine.

The debate is not about abolishing technology, but about regulating it. Experts advocate for specific laws that define civil liability in cases of error, require external audits of algorithms, and ensure that AI is used as a support tool, not as a substitute for medical judgment. Without this, the risk of impersonal, bureaucratic medicine is real.

The future: between precision and humanization

The trend is for technology to become even more present. The global market for AI in genomics is expected to grow 25% per year until 2030 (MarketsandMarkets, 2026). Smaller hospitals are already studying pilot projects. The Ministry of Health is discussing a national genomics program with AI support for underserved regions.

China leads this movement. The country uses AI on a large scale for genomic sequencing in public hospitals, processing more than 100 million sequencing runs per year. Europe is taking a more cautious approach, with the European Union's AI Act classifying genetic diagnoses as "high risk" and requiring mandatory human oversight. Brazil is in the middle of the road, without specific federal legislation.

The answer to the dilemma may lie in balance. Hospitals that achieved the best results combined technology with clear usage protocols, external audits, and continuous training of medical teams. Hospital Israelita Albert Einstein, for example, created an ethics committee to oversee the use of AI systems (Hospital Israelita Albert Einstein, 2026).

What you need to know before trusting (or fearing) AI in genomics

The decision about the use of AI in healthcare cannot be left solely to hospitals and technology companies. The public needs to understand what is at stake. Datafolha data (2026) show that the majority wants precision, but also wants human care. These are two desires that need to be reconciled through mature public policies.

The coming years will be decisive. The technology already exists and works. The question is how we will use it. If Brazil follows the path of transparency and social control, AI can be a powerful tool for protecting life. If the path is one of unchecked automation, the price may be too high for the physician-patient relationship. The choice is ours, and the clock is ticking.