Medical diagnosis has long depended on the expertise of health professionals to interpret symptoms, images and test results. Today, artificial intelligence (AI) is changing this field by offering diagnostic solutions assisted by algorithms that analyze massive data, detect complex patterns and improve diagnostic accuracy. This transformation concerns various pathologies ranging from cardiovascular diseases to neurological disorders, including infections and cancer. With the integration of AI, the field of diagnosis is evolving towards faster, more personalized and more reliable medicine.
In many areas of health, diagnosis can be complex, time-consuming, and subject to human error. For example, subtle abnormalities in medical images or biological data can easily be missed by specialists, especially when there is an overload of work. AI makes it possible to remedy these limitations by providing tools capable of processing large volumes of data and identifying weak signals that the human eye could ignore. However, its integration into medical practices poses challenges, such as the interpretability of results and the management of data biases.
State of play of a planned revolution.
AI, Biomarkers, and Predictive Medicine
Predictive medicine, facilitated by artificial intelligence (AI), makes it possible to anticipate the risk of diseases even before the onset of symptoms. This approach is based on the analysis of biomarkers — measurable biological indicators linked to specific health conditions — that can be detected in a patient's genetic, molecular, or even environmental data. By exploiting this data through AI models, clinicians can not only diagnose diseases, but also anticipate their development and adapt treatments to the specific characteristics of each individual.
Identifying biomarkers using AI algorithms
AI makes it possible to analyze massive volumes of genetic and molecular data to identify biomarkers indicative of certain diseases. In oncology, for example, mutations in the BRCA1 and BRCA2 genes are associated with an increased risk of breast and ovarian cancer. Machine learning models are used to explore genetic sequences and detect rare mutations that could escape conventional analyses. These tools thus offer the ability to reveal new biomarkers related to hereditary predisposition to cancer or other serious diseases, such as cardiovascular and neurodegenerative diseases.
In predictive medicine, unsupervised learning algorithms, such as clustering (1), play a fundamental role in patient stratification. Clustering makes it possible to group patients with similar genetic profiles and to categorize them into subgroups with specific disease risks. For example, patients can be classified into cancer subtypes based on their genetic mutations, allowing for a better understanding of variations in treatment response and disease progression. This stratification is critical in precision oncology, where treatments can be customized to maximize effectiveness and minimize side effects.
Clinical applications of the use of biomarkers
By exploiting databases containing thousands of biomolecular patient profiles, AI can identify subtle associations between certain biomarkers and the probability of developing cancer. For example, deep neural networks (2) can analyze large amounts of genetic and epigenetic data to detect molecular signatures associated with increased cancer risks. Unsupervised learning, including clustering, is used to group patients based on their biomarker profiles and reveal cancer subtypes with particular predisposition. This approach is essential for stratifying patients in clinical trials and for customizing treatment options based on the specific biology of each tumor. With the support of AI, researchers are exploring composite biomarkers — signatures made up of several types of biomarkers (genetic, epigenetic, protein) — to increase the sensitivity and specificity of screening tests. These composite biomarkers, analyzed using advanced algorithms, make it possible to detect several types of cancer from a simple blood sample, a promising approach known as a “liquid biopsy.”
Using biomarkers to predict hereditary diseases revolutionizes risk prevention and management. In the context of genetic diseases such as familial cardiomyopathies or hereditary metabolic diseases, AI makes it possible to predict the probability of transmission and occurrence of these pathologies by analyzing the associated genetic mutations. For example, machine learning models can assess the risks associated with genetic mutations much more quickly than traditional screening methods. This allows doctors to recommend regular checkups, lifestyle adjustments, or preventative treatments to delay the onset of the disease.
Beyond the prediction of risks, the biomarkers identified by AI also contribute to Predicting patient response to treatments. In oncology, for example, certain genetic and molecular biomarkers indicate whether a patient is likely to respond positively to immunotherapy or targeted therapy. AI algorithms analyze thousands of data from patient cohorts to build response profiles, giving clinicians valuable tools to guide therapeutic strategies and avoid ineffective treatments. This not only improves the chances of therapeutic success, but also limits the side effects associated with unsuitable treatments.
Advances in AI in medical imaging diagnostics
One of the major contributions of AI in medical imaging is the ability to detect subtle anomalies, sometimes invisible to the human eye. Convolutional neural networks (CNNs) (3), in particular, are used to identify pathologies such as tumors, lesions, and other abnormalities in x-rays, MRIs, and scans. These models reduce the time required for diagnosis while increasing accuracy. For example, using AI to analyze lung scans has shown increased efficiency in detecting nodules, an early sign of lung cancer.
Segmentation of medical images and radiomics
Segmentation techniques make it possible to precisely delineate anatomical structures and anomalies in images. Deep learning-based models, such as the U-Net architecture (4), are commonly used to segment organs and tumors. In oncology, these techniques facilitate detailed analysis of tumor sizes and shapes, which is essential for planning treatments and evaluating their effectiveness. Segmentation techniques also make it possible to identify areas of abnormal density, thus improving the monitoring and treatment of neurological pathologies and cardiovascular diseases. The so-called “radiomics” approach (5) is one of the most advanced applications of AI in medical imaging. By extracting and analyzing a broad range of image characteristics (such as texture, shape, and intensity), AI can decipher the tumor phenotype, making it easier to predict disease progression and personalize treatments. For example, in lung cancers, radiomics makes it possible to predict patient survival and treatment responses by analyzing CT images.
Diagnostic support and automation
AI provides valuable support to radiologists by automating routine tasks and generating preliminary reports based on image analysis. These systems can indicate risk areas in mammograms or chest scans, offering clinicians invaluable assistance in focusing their efforts where needed. In addition, AI facilitates predictive analytics, making it possible to anticipate the evolution of pathologies based on past images and historical clinical data, and supports decision-making through evidence-based recommendations.
However, the integration of AI in medical imaging requires overcoming several challenges, particularly in terms of model interpretability and data quality. Imaging data should be accurate and labeled consistently to ensure reliable results. Despite these challenges, AI continues to show immense potential to improve the efficiency and accuracy of medical diagnosis, paving the way for more predictive and personalized medicine.
The challenges of AI in medicine: Regulation, data access and more
While artificial intelligence (AI) offers impressive opportunities to transform medicine, it faces major challenges that hamper its deployment and adoption. These obstacles include regulatory aspects, access to data and the need to ensure ethics and security in its application.
Strict regulations and legal complexity
The adoption of AI in medicine is closely monitored by regulators to ensure patient safety and the effectiveness of the proposed solutions. However, current approval processes, often based on frameworks designed for traditional medical devices, are not fully adapted to ever-changing AI technologies. This poses several problems:
- Frequent model updates : unlike traditional devices, AI algorithms require regular updates to remain efficient and relevant. Regulating these updates without delaying innovation remains a complex challenge.
- Transparency and explainability : AI models, especially deep neural networks, are often perceived as “black boxes.” This opacity complicates regulatory validation, as it is difficult for regulators to understand and explain algorithm decisions.
Initiatives, such as the development of explainable AI frameworks, aim to meet these requirements, but there is still a long way to go before they are systematically incorporated into regulations.
Access to medical data: a major challenge for AI
Medical AI relies on the analysis of large amounts of data to identify patterns and generate reliable predictions. However, accessing health data is complex for several reasons:
- Patient confidentiality : medical data is highly sensitive, and laws like the GDPR in Europe strictly regulate its use to protect the privacy of patients. This limits the amount of data available to train and validate AI models.
- Fragmentation of data : medical data is often scattered across different hospitals and clinics, and is rarely standardized. This fragmentation complicates the training of AI models that can operate effectively in a variety of clinical contexts.
- Data quality : to ensure accurate predictions, the data used must be of high quality and well labeled. However, input errors, missing diagnoses or the absence of standardization affect the reliability of algorithms.
Integrating AI into medical practices requires a high level of technological infrastructure, which poses financial and logistical challenges for many healthcare institutions. Indeed, many hospitals lack the technical resources to store and process the volumes of data required by AI models. Additionally, medical personnel need to be trained to use and interpret AI tools, which requires time and training effort. The adoption of AI therefore requires institutional and governmental support to fund the necessary infrastructure and to train health professionals in the use of AI technologies in their daily practice.
AI has the potential to transform medical diagnosis by making processes faster, more accurate, and accessible to a greater number of patients. Whether in cardiovascular diseases, neurological disorders, infections or other areas, AI is proving to be a powerful ally for health professionals. However, to fully realize this potential, it is still necessary to address technological, ethical, and regulatory challenges in order to ensure responsible and effective adoption. AI, supported by ambitious policies, will then be able to redefine medical practices for faster care and a significant improvement in clinical outcomes.
Notions explained:
(1) Clustering architecture is a method used in artificial intelligence to group similar items together, without being told in advance what to look for. Imagine having a big box of pencils of all colors, and asking someone to organize them by color, but without giving them a color model or specific rules. He will just look at the pencils, compare the colors, and make groups that seem logical to him.
(2) The deep neural networks are a type of artificial intelligence that works much like our brains to analyze complex information. They are made up of layers of units called “neurons” (hence the name “neural networks”). These neurons are connected to each other in several layers, a bit like a mille-feuille, hence the expression “deep” to refer to the complexity of the model.
(3) The convolutional neural networks, or CNN (for Convolutional Neural Networks), are a type of neural network specially designed to analyze images. Their name comes from the “convolution” technique, which consists in dividing an image into small parts (or “filters”) to understand the details. These networks are very effective at recognizing objects, shapes, or patterns in images, making them very popular in applications like facial recognition, tumor detection, or even identifying animals in photos.
(4) U-Net architecture is a type of neural network developed especially for image segmentation, especially in medical imaging. Think of it as a tool that, by looking at a complex image (like an MRI scan), can identify and precisely delineate specific areas, such as the contours of a tumor, organ, or lesion.
(5) The Radiomics architecture is an approach used primarily in medical imaging to extract detailed information that is invisible to the human eye from medical images, such as those from an MRI, CT scan, or X-ray. Basically, it's a way to use medical images to get a lot more information than just “it looks normal” or “there's a tumor.”
Sources:
- Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review
- AI-Assisted Detection of Biomarkers by Sensors and Biosensors for Early Diagnosis and Monitoring
- Advancements in AI for Medical Imaging: Transforming Diagnosis and Treatment
- The role of artificial intelligence in medical imaging research
