Aifa Report

Artificial intelligence: already integrated in 62% of pharmaceutical companies

Expected growth of 45% over the next five years: research and development departments most involved with the aim of accelerating the development of new molecules

by Paolo Castiglia

9 March 2026

3' min read

Translated by AI

Versione italiana

Key points

The role of regulatory agencies
More efficient clinical trials and precision medicine
Sovran-AI: Ia's role in reducing waiting lists

3' min read

Translated by AI

Versione italiana

"Today, 62% of pharmaceutical companies already integrate artificial intelligence into their research and development (R&D) departments, with 45% growth expected in the next five years. The reason? The advantages are obvious: faster, more accurate, cheaper, and the ability to more reliably predict the efficacy of a treatment even before human trials'. This was stated by the Italian Medicines Agency, which published the dossier 'Artificial Intelligence and Health. How AI is revolutionising pharmaceutical research, precision medicine and the future of global health'.

The role of regulatory agencies

In this scenario of great change, drug regulatory agencies are called upon to play a leading role. Aifa, as well as the European Ema and the American Fda, are paying great attention to the potential of technology to ensure faster, ethical and data-driven decisions. If the Ema has already launched the five-year 'Data and AI' plan to integrate the algorithm into regulatory processes, the Aifa is progressively adopting predictive tools with the dual aim of speeding up the evaluation of regulatory dossiers and supporting Hta (Health Technology Assessment), analysing the clinical benefits and economic impact of new therapies.

According to Aifa, therefore, the pharmaceutical industry 'is today one of the most advanced spearheads of digital innovation. The industry, which is research-intensive by nature, has embraced artificial intelligence as an indispensable ally'.

The most promising fields of application of AI in healthcare range from the initial stages of laboratory research, through clinical trials to everyday medical practice. According to the Aifa dossier, for example, thanks to deep neural networks, it is possible to analyse millions of molecules and predict their failure in silico (virtually), immediately discarding ineffective or toxic compounds. The advantage is that research time and costs are drastically reduced, concentrating resources only on the most promising candidates.

More efficient clinical trials and precision medicine

Through specific platforms, underlines the Aifa report, it is possible to analyse millions of medical records in just a few minutes to identify patients suitable for enrolment in a clinical trial, overcoming the recruitment difficulties that often slow down the conduct of studies. AI also makes it possible to move part of the trial from the real world to the virtual world (in silico). Indeed, the advent of 'Virtual Trials' and 'Digital Twins' makes it possible to predict the response to a treatment before the actual administration of the drug, drastically reducing the need for animal and human testing in the preliminary stages of studies.

Thanks to the integration of clinical, genetic and environmental data, it is also possible to overcome the 'one-size-fits-all' approach to therapies in order to tailor them to the patient. Personalising treatments means reducing errors and risks of toxicity, but also optimising resources. It is therefore an investment in the health and sustainability of the healthcare system.

Sovran-AI: Ia's role in reducing waiting lists

But Ia can also be applied by improving the flow of reservations in the Cup and reducing waiting lists. Along these lines, a project developed by Sovran-AI is moving in response to the need to automate interactions and reduce the operational load on operators, improve the usability of healthcare services and guarantee rapid, correct and uniform responses to patients. The integration of Ia into the processes, through the adoption of automated, standardised procedures that are always aligned with internal protocols, actually enhances the overall capacity of the Cup.

"We have just concluded," explains Sovran-AI CEO Pierluigi Ghiani, "the experimental phase of the followup of the bookings in continuity with the operational flow that generated and managed them, and we ourselves were really impressed by the impact: in addition to the reduction in no-shows, the freed slots were reallocated and assigned to other patients who needed them. A positive result both from the point of view of patient service and the economic return to the facility, which was able to avoid losing those visits for which it had already allocated resources".

Already operational in various healthcare settings, the platform can be extended to any department or service, adapted to public, accredited and private healthcare facilities, and also integrated with regional healthcare systems in terms of operational efficiency, producing concrete and measurable benefits in terms of reduced costs related to repetitive and low-value front-office activities, thanks to the complete automation of standard information and administrative flows