Imagine a world where going to the doctor is more like a conversation with a friend than a stressful consultation. A world where diagnoses are more accurate, treatments more personalized, and where prevention is at the heart of our daily lives. No need to dream, this world is being built thanks to artificial intelligence (AI) in the field of health.
Quietly but surely, AI is making its way into our hospitals, pharmacies and even our mobile applications. It analyzes tons of data to help doctors make the best decisions, detects illnesses before symptoms even appear, and guides us to healthier lives without us even realizing it.
Everyone talks about it, uses sometimes barbaric terms, but how many people really understand how it works and how it is used? Before understanding its use in the world of health, some explanations are needed.
What is artificial intelligence?
Artificial intelligence refers to computer systems that can perform tasks that are traditionally associated with human intelligence, such as making predictions, identifying objects, interpreting speech, and generating natural language. AI systems learn to do this by processing huge amounts of data and looking for patterns to model in their own decision-making. In many cases, humans oversee the AI learning process, reinforcing good decisions and discouraging bad ones, but some AI systems are designed to learn without supervision.
Ok... and what does that mean in practice?
Imagine if your computer could do things that only a human being could do before. For example, predicting the weather tomorrow, recognizing the face of your grandchildren in a photo, understanding what you are saying, or even writing complete sentences as if it were a person. That's artificial intelligence, or AI.
How can a machine learn?
By looking at and analyzing a huge amount of information. Think of it like a child learning to recognize animals by looking at hundreds of images of cats, dogs, and birds. But instead of hundreds, computers can see millions of them in a very short time.
What is the place for humans in this learning process?
Often, humans help these systems learn. They show the computer what is correct and what is not, much like a teacher guides a student. For example, if the AI needs to recognize fruits, it is shown images by saying “That's an apple” or “That's a banana.” But sometimes computers learn by themselves, without help, by finding patterns and similarities on their own.
A permanent improvement.
Over time, AI gets better at the tasks it does. The more information it receives, the more accurate it becomes. It can adapt to new situations without having to be reprogrammed each time. It's a bit like someone who becomes an expert in a field by doing a lot of practice.
How does artificial intelligence work?
Artificial intelligence, or AI, works through special computer programs called algorithms, and uses a lot of data. An algorithm is a series of specific steps or instructions designed to solve a specific problem or accomplish a particular task. You can think of it as a cooking recipe (the instructions form the algorithm) that uses ingredients (the data) to create a dish (the task completed).
To make this work, artificial intelligence relies on three components: machine learning (or Machine Learning), a neural network, deep learning and natural language processing (NLP). I am explaining everything to you.
Machine learning (Machine Learning)
Machine learning is a way for computers to learn without being programmed for each specific task. They use data to find out how to do the task on their own.
- How does it work? Instead of telling the computer exactly what to do, it is given lots of examples, and it comes up with the rules by itself.
- Example: if we want the computer to recognize numbers written by hand, we show it thousands of images of numbers, and it learns to recognize the shapes of each number.
There are two main types of machine learning:
- Supervised learning: the computer is provided with data with the correct answers. It's like a teacher correcting a student's homework.
- Example: we show the computer images of apples and bananas by telling it which are apples and which are bananas. In this way he learns to distinguish between them.
- Unsupervised learning: The computer is given unanswered data, and it must find the patterns on its own.
- Example: The computer analyzes data on customers in a store and finds groups of people with similar buying habits, without knowing in advance which groups these groups are.
Neural networks
Neural networks are programs that mimic how the human brain works. It is an algorithmic mimic of the functions of the human brain.
- Structure: they are composed of numerous small units called “neurons” that are connected to each other.
- How it works: each neuron receives information, transforms it, and then sends it to the next neurons. By adjusting the connections between neurons, the network learns to complete tasks.
- Example: recognizing faces in photos. The neural network learns to identify facial features by analyzing numerous images.
Deep Learning
Deep learning is an advanced form of machine learning that uses neural networks with numerous layers. By adjusting the strength of the connections between these neurons, the network can learn to recognize complex patterns in data, make predictions based on new inputs, and even learn from its mistakes.
- Multiple layers: the more layers there are, the more complex patterns the system can recognize.
- Advanced recognition: this allows the computer to understand images, text, or speech with great precision.
- Example: voice assistants like Siri or Google Assistant use deep learning to understand what you say and respond to you in a relevant way.
Natural Language Processing (NLP)
Natural language processing involves teaching computers to understand and produce written and spoken language in a manner similar to humans. NLP combines computer science, linguistics, machine learning, and deep learning concepts to help computers analyze unstructured text or voice data and extract relevant information from it
- Understand text and speech: The computer analyzes words, sentences, and context to understand what is being said.
- Applications:
- Voice assistants: Like Alexa or Siri, who understand and answer your questions.
- Machine translation: Convert text from one language to another.
- Spam detection: Identify and filter unwanted emails.
- Example: When you talk to your phone to send a message, NLP understands your words.
Concrete examples in health?
Artificial intelligence is transforming the health sector, here are some concrete uses.
Assisted diagnosis
Medical image analysis: deep learning algorithms are used to analyze radiological images such as X-rays, MRIs, and scanners. For example, AI can detect tumors, fractures, or lung abnormalities with an accuracy comparable to, or even greater than, that of human radiologists.
Example: AI systems have been developed to identify early signs of breast cancer on mammograms, helping with earlier detection and better management.
Detection of eye diseases: AI analyzes images of the retina to detect conditions such as diabetic retinopathy or AMD.
Example: Google Health has developed an algorithm that can screen for diabetic retinopathy with great precision, which is particularly useful in areas where ophthalmologists are rare.
Assistance in the treatment and follow-up of patients
Customized T-treatment: AI can help personalize treatment plans by analyzing a patient's genetic, medical, and environmental data.
Example: in oncology, AI can recommend targeted therapies based on the genetic profile of the patient's tumor.
Medication Management: AI applications remind patients to take their medications, monitor side effects, and adjust doses as needed.
Example: the “Medisafe” application uses AI to personalize medication reminders and provide information on drug interactions.
Smart medical devices
Connected devices and sensors: Wearable devices such as smart watches and fitness bracelets monitor vital signs such as heart rate, sleep patterns, and physical activity in real time.
Example: Apple Watch can detect irregular heart rhythms, such as atrial fibrillation, and alert the user to see a doctor.
Implantable sensors: sensors can be installed to continuously monitor specific parameters.
Example: devices measure blood sugar levels in diabetic patients in real time and can automatically administer insulin.
AI in medicine is not there to replace doctors, but to help them provide better quality care. By making diagnoses more accurate, personalizing treatments, and optimizing patient management, AI is transforming health in a positive way. It's like having an ultra-smart assistant by your side, ready to help you every step of the way in your health journey.
