AI does not “replace” healthcare personnel: multiplies its reach. It automates repetitive tasks, finds patterns in data that are impossible to review manually, and suggests decisions that the clinical team then validates. The interesting thing: we're no longer talking about promises, but of results in hospitals and national programs.
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Real-life examples and cases of AI in medicine
1) Early detection of breast cancer
The artificial intelligence (AI) applied to the mammography is improving early detection of breast cancer, reducing workload and maintaining or even improving diagnostic accuracy. A randomized trial in Sweden demonstrated that AI, as a supporting reader, is not only clinically safe, but also reduces radiologists' workload by half.
The recent implementations, have increased the detection of cancers without increasing the false positives. In addition, advanced models now make it possible to more efficiently select patients who need a magnetic resonance imaging complementary screening after a negative mammogram. The clinical impact is clear: earlier detection, fewer unnecessary second readings, and a triage more efficient.
2) Diabetic retinopathy in national programs
The artificial intelligence (AI) is being used on a large scale in the screening for diabetic retinopathy, improving the detection and management of this eye disease. In Thailand, AI has been implemented in multiple centers with real-time reading, demonstrating its viability and positive impact on the public health system, according to studies in PubMed.
Recent integrations combine vision and language, optimizing workflows in primary care, facilitating faster screenings, more precise derivations and a minor retinal saturation.
3) Dermatology: melanoma and skin cancer
Artificial intelligence (AI) is revolutionizing the diagnosis of melanoma and skin cancer, providing support even with hard-to-detect injuries. A multicenter study conducted in 8 hospitals showed that AI algorithms outperformed dermatologists in the detection of melanoma in complex cases, which highlights its potential to improve diagnostic accuracy.
Regulatory advances and the use of techniques such as federated learning They are facilitating the integration of AI-assisted devices into primary care, improving diagnostic privacy and efficiency, and optimizing the triage process.
4) Cardiology: atrial fibrillation with wearables
The smartwatches and Portable ECGs With artificial intelligence they are improving early detection of atrial fibrillation (AF), allowing passive alerts that facilitate continuous monitoring. Recent studies, such as those of Oxford Academic and ScienceDirect, have demonstrated improvements in the detection of AF episodes using predictive models and clock ECG.
Although the benefits are clear, diagnostic confirmation and reduction of false positives. This is leading to a clinical approach to remote monitoring and event prevention, allowing for faster referrals and more proactive disease management.
5) Sepsis: early warnings that change outcomes
The sepsis requires rapid intervention to improve outcomes, and AI systems such as COMPOSER have been shown to reduce mortality and improve compliance with treatment protocols in hospitals. A study highlights how these systems help manage the “golden hour” more effectively.
Recent research validate predictive models to anticipate the onset and mortality of sepsis. The key is to integrate these models into the clinical flow, as in the electronic health records (EHR), with clear and actionable alerts that optimize its actual use.
These cases make it clear that AI in medicine is already present and is a clear ally for healthcare professionals and scientists.
En Maxymia acercamos herramientas de IA aplicada a profesionales y equipos de investigación. Nuestra misión es democratizar la IA con cursos útiles, apoyo personalizado y certificación.
