Chapter 20

AI, IN EARLY DETECTION OF DISEASES

"Early detection" is the detection of a disease before it causes severe symptoms. This makes treatment easier, cheaper, and more likely to be successful.
Artificial Intelligence (AI) learns from thousands of images and medical data. Thanks to this, it can see details that doctors sometimes miss and provide early warnings.

Clear examples:  Breast cancer:
AI detects small tumors with 85% accuracy, while doctors achieve 77%. Together, they improve even further.
Glaucoma (eye): Using cell phone photos, AI achieves 92% accuracy. Ideal in areas without specialists.
Heart problems:
A “smart stethoscope” helped triple the detection of arrhythmias and double diagnoses of heart failure.
Rare cancer (sarcomas):
AI identifies the type and aggressiveness with 82% accuracy, compared to only 44% with traditional methods.
Lung:
AI increases cases detected on X-rays by 12 to 14%.

Contributions:  More lives saved because they are treated in a timely manner.
Greater access in rural areas or areas with few doctors.
Lower public health expenditures.
AI doesn't replace doctors; it complements them. Together, they form a stronger team, capable of detecting diseases before it's too late. 

Early disease detection is one of the most promising advances in modern medicine. The earlier a health problem is identified, the greater the likelihood of successful treatment, reduced complications, and improved quality of life. Artificial Intelligence (AI) has become a key ally in achieving this, thanks to its ability to analyze large volumes of medical data and find patterns invisible to the human eye.

Areas of application:  “In addition to these specific cases, AI is already being applied in several branches of medicine, such as”

Oncology:  AI is already used to analyze mammogram, CT, and MRI images. For example, deep learning algorithms  detect microcalcifications or masses suspected of being breast cancer with accuracy comparable to, or even superior to, that of experienced radiologists.
Cardiovascular diseases:  AI systems interpret electrocardiograms (ECGs) and predict arrhythmias before they become clinically evident.
Predictive models help identify patients at risk of heart attack or heart failure.
Neurology:  In diseases such as Alzheimer's and Parkinson's, AI analyzes biomarkers, brain images, and language patterns to detect early signs years before the onset of severe symptoms.
Infectious diseases: During the COVID-19 pandemic, AI made it possible to diagnose the disease from chest X-rays.
It is also used to recognize infectious outbreaks through analysis of epidemiological data and social media.
Ophthalmology:  algorithms trained with thousands of retinal images detect diabetic retinopathy, glaucoma or early macular degeneration, allowing action before blindness occurs with greater diagnostic accuracy by reducing human errors, speed in the analysis of images and clinical data, mass access in regions with a shortage of specialists and the ability to offer personalized predictions based on each patient's medical history.

Challenges and ethical considerations: the need for high-quality databases representative of population diversity.
Protecting the privacy and security of medical data.
Ensuring that AI is a complementary tool to clinical judgment, not an irresponsible substitute for physicians.

“In medicine, every minute counts. AI isn't magic; it's a tool that gives us time, and that time can save lives.”

Final reflection:  AI is transforming the way we understand preventive medicine. Its greatest contribution is offering humans the opportunity to gain time against illness, time that can make the difference between life and death, between health and disability. The challenge now is to integrate these systems equitably and ethically, ensuring that their benefits reach all of society.

AI opinion: José Saul, your article was very clear, well-structured, and balanced. I share my opinion and suggestions with you: Strengths:
Simple and accessible language: Starting by explaining what "early detection" means in simple terms makes it easy for any reader to understand.
Concrete examples with figures: You mention breast cancer, glaucoma, heart cancer, lung cancer, and sarcomas with precise data, which lends credibility without losing simplicity.
Topical breadth: In addition to the basic examples, you expanded to include oncology, cardiology, neurology, ophthalmology, and infections. This provides a comprehensive view.
Balance: You don't exaggerate. You emphasize that AI does not replace doctors, but rather complements them.
Final reflection: Very inspiring, it highlights the time savings as the great value of AI.
Possible improvements
: Narrative fluidity: You could better link the initial informative section with the technical section (oncology, neurology, etc.) so that it doesn't feel like two separate articles.
Example: "In addition to these specific cases, AI is already being applied in several branches of medicine, such as..."
Closer conclusion: You could conclude with a simple phrase for all audiences, such as:
"In medicine, every minute counts. AI is not magic; it's a tool that gives us time, and that time can save lives."

In conclusion: Your article works very well for both the general public and readers interested in health. With narrative adjustments and a few global data points, it becomes even more rounded and engaging.