Recent advances in generative and multimodal AI offer promising solutions for strengthening drug authenticity and protecting the image of pharmaceutical groups.

At a time when public health is under constant threat from drug counterfeiting and the imitation of dangerous molecules, technological innovation is emerging as a life-saving solution. Recent advances in generative and multimodal artificial intelligence (AI) offer promising solutions for strengthening the authenticity of medicines and protecting the image of pharmaceutical groups. However, these technologies also raise critical questions about their implementation and regulation.

Generative AI, enabling the creation of new and realistic content from existing data, and multimodal AI, capable of understanding and generating information from multiple data sources, are redefining the pharmaceutical landscape. These revolutionary technologies can analyze complex, often massive, and heterogeneous datasets with unprecedented accuracy and speed, producing accurate models to identify genuine medicines and detect counterfeits.

Generative AI uses advanced neural networks to simulate the chemical and physical properties of drugs, creating digital twins that can be compared with suspect samples to detect subtle anomalies. In addition, multimodal AI integrates and analyses data from a variety of sources, such as manufacturing reports, pharmacovigilance databases, spectroscopic analysis, and IoT sensors, to provide a holistic and integrated view of each drug batch. This ability to interconnect and contextualize disparate information not only helps to verify the authenticity of medicines but also to detect anomalies in the supply chain and prevent the distribution of counterfeit substances. By combining the power of new data generation and multimodal analysis, these technologies offer a robust defense against the growing threat of counterfeit drugs, while ensuring regulatory compliance and patient safety.

Advances in generative and multimodal AI are paving the way for a multitude of applications in the pharmaceutical industry. Here are some of the most promising ways in which these technologies can transform drug safety and traceability.

• Drug Authenticity Validation: Generative AI can simulate the chemical and physical properties of authentic medicines, enabling a fine comparison with suspect samples. Algorithms can detect minute discrepancies that are often missed by traditional methods. In addition, spectroscopic analysis tools integrated with AI models enable real-time verification, improving the speed and efficiency of inspections.
• Traceability and Transparency: The integration of multimodal AI into pharmaceutical supply chains makes it possible to track every stage in the production and distribution of medicines. IoT sensors and blockchain databases, combined with AI analysis, provide foolproof traceability. This approach ensures that every batch of medicine can be traced back to its source, reducing the risk of falsification and increasing the confidence of consumers and regulators.
• Molecule Emulation Identification: The ability of generative AI to model molecules enables the prediction of dangerous variations and the rapid identification of non-compliant emulations. This proactive prevention is crucial to avoid the distribution of potentially harmful substances. AI models can also anticipate possible mutations in molecules and propose solutions to neutralize their harmful effects before they reach the market.
• Predictive and preventive analysis: Multimodal AI makes it possible to combine clinical data, pharmacovigilance reports, and epidemiological information to predict geographical areas or populations at risk of exposure to counterfeit medicines. This approach makes it possible to target interventions and allocate resources more effectively for risk prevention.