How is artificial intelligence set to transform clinical research? A look back at the EDHEC conference in spring 2025

Artificial intelligence is increasingly shaking up the foundations of modern medicine. From digital twins capable of simulating a patient's progress to synthetic data arms used to replace traditional control groups in clinical trials, medical research is entering an era of radical transformation.

But these innovations raise as many questions as they offer promises: can a treatment be validated without a real ‘control group’? How can the reliability and ethics of algorithms be guaranteed? Is France ready to remain competitive in this global race, facing already well-established powers?

To decipher these challenges, EDHEC Business School, through its Management in Innovative Health Chair, and Median Technologies organised a conference-debate bringing together key players in the sector:

• Owkin, French-American startup pioneering AI in oncology;
• Servier, long-standing player in pharmaceutical innovation;
• the Ministry of Health, through the Ségur digital initiative;
• and the think tank Ethik-IA, committed to responsible AI in healthcare.

Through open and candid discussions, this evening event helped to outline the contours of tomorrow's clinical research — research enhanced by data, driven by algorithms, but guided by humans.

A healthcare system undergoing transformation

The healthcare sector is currently undergoing major transformations: an explosion of medical data, the development of personalised medicine, accelerated therapeutic innovations, increased regulatory pressures and heightened patient expectations.

In this context, artificial intelligence is emerging as a strategic lever, capable of reshaping the contours of prevention, diagnosis and care, as well as research.

At the intersection of these developments, a new approach has emerged that has become central: translational medicine. Its ambition is to foster continuous dialogue between fundamental science, clinical data, real-world applications and technology in order to accelerate the transition from the laboratory to the patient's bedside. AI, with its ability to cross-reference large amounts of heterogeneous data, is becoming a powerful catalyst for this dynamic.

Between technical promises and regulatory constraints

‘Artificial intelligence is everywhere in our daily lives, but in clinical research, it remains a subject of exploration,’ introduces Caroline Baufour, Head of Innovation – Clinical Development at Servier. While technology is advancing rapidly, its integration into trial protocols remains subject to numerous constraints, including regulatory validation, traceability and data robustness.

Antoine Iannessi, medical director at Median Technologies, confirms this discrepancy: 'AI is ready. What is not yet ready are the frameworks for its use, the testing infrastructure and the validation channels'. In other words, clinical research is on the verge of a technological leap, but this leap requires profound adjustments to organisational, regulatory and cooperation models.

AI in action: from diagnosis to prediction

Andy Karabajakian, Director of Oncology at Owkin, embodies a more operational vision: ‘In certain fields, such as radiology and digital pathology, AI is already a clinical reality.’

The French-American company has developed RlapseRisk, a deep learning tool capable of predicting the risk of relapse in cancer patients, which is currently being tested at Bicêtre Hospital (AP-HP). This innovative technology still needs to pass regulatory certification before it can be fully integrated into the healthcare pathway.

This predictive logic, which lies at the heart of medical AI, opens up immense possibilities: identifying patients who will respond to treatment earlier, avoiding unnecessary side effects, and optimising the design of clinical trials. But it also raises a fundamental question: can we entrust medical decisions to algorithms? And under what conditions?

A technology in search of legitimacy

Trust is quickly emerging as one of the major challenges. ‘A study conducted by EDHEC shows that 40% of women surveyed say they do not trust AI tools in healthcare,’ says Loïck Menvielle, professor at EDHEC and director of the Management in Innovative Health Chair. This figure is indicative of the barriers to acceptance.

Pierre Loulergue, infectious disease specialist and member of the Ethik-IA collective, insists: ‘We are at a turning point. Now is the time to lay the right foundations: regulate use, ensure transparency and improve explainability.’

This view is shared by Andy Karabajakian, who advocates for AI that is ‘traceable and understandable,’ built on explainable models and clear decision chains.

But this requires a different way of thinking about the architecture of tools, emphasises Antoine Iannessi: ‘You can't add explainability after the fact. It has to be designed in from the start.’

Data to be structured, a system to be streamlined

At the heart of the debate is health data, the essential fuel for artificial intelligence. However, this resource is still scarce, scattered and difficult to share. ‘For AI to work, you need reliable, comprehensive and representative data,’ insists Caroline Baufour.

This is a challenge that Olivier Clatz, director of Ségur numérique, is actively working on. Thanks to the Mon espace santé platform, more than 250 million medical documents have already been exchanged since the end of 2023. ‘France now has one of the most advanced digital ecosystems in Europe, and we are going to further enhance it, particularly through imaging and genetic data.’

Developing clinical research that lives up to its promises

What emerged from the discussions was not a distant future, but a goal to be achieved today. For the speakers, the future of AI in clinical research will not be decided solely in laboratories, but in our collective ability to meet five essential conditions: reliable data, transparent models, active ethics, consistent regulation and, above all, a strong alliance between science, industry and public institutions.

Faced with the temptation to move quickly, the challenge is to move correctly. Artificial intelligence must not simply accelerate clinical research: it must elevate it, enlighten it, make it more accurate, more accessible and more equitable.

Only then will AI be able to deliver on its promise: not to replace medicine, but to extend it, strengthen it and, perhaps, rehumanise it.