Artificial intelligence is revolutionising the medical sector, offering innovative solutions for diagnostics, patient monitoring and care optimisation. Hospitals, clinics, laboratories and healthcare companies are relying increasingly on these technologies to improve treatment efficiency and patient care. However, their adoption remains complex and many stakeholders struggle to structure their approach to fully leverage them.
Dans un service d’oncologie d’un grand hôpital, un radiologue examine une IRM. Une anomalie apparaît sur l’image. Est-ce une tumeur bénigne ou maligne ? Le diagnostic est délicat. L’expertise humaine est essentielle, mais l’analyse des données médicales est devenue si complexe qu’une Artificial intelligence second reading could provide an answer in seconds, with an accuracy greater than 95 %. However, this technology is not yet used in this establishment.
The’Artificial intelligence is not a futuristic concept. It is already here, transforming the way healthcare professionals diagnose, treat, and monitor their patients. Yet, adoption remains slow and patchy. Why would a hospital use AI to optimise its stock of medical supplies, but not to assist its doctors in detecting serious illnesses? How is it that pharmaceutical research uses advanced algorithms to identify new molecules, yet many institutions still struggle to structure their patient data?
The question is no longer whether AI has a role to play in medicine, but How to integrate it effectively into daily practices.
According to PAC, sovereign AI refers to AI solutions governed and controlled within a defined jurisdiction (Europe/France), guaranteeing control over data and infrastructures.
For general management :
The medical sector is experiencing a data explosion. Each patient generates thousands of pieces of information: electronic health records, laboratory results, medical imaging, data from connected sensors, and so on. However, exploiting this data effectively represents a colossal challenge For healthcare professionals.
In parallel, hospitals and clinics are facing a staff shortages and increasing workload. AI could alleviate this pressure by automating certain tasks and improving care management. But how to move from potential to concrete action ?
In pharmaceuticals and biotechnology, artificial intelligence is accelerating the development of new treatments. Where A clinical trial could take several years, AI allows for the analysis of millions of genetic and molecular data points in just a few days. However, these advancements remain too often siloed and do not always benefit the entire care pathway.
It is therefore becoming urgent for the medical sector to’adopt a clear and pragmatic strategy to take advantage of these innovations.
Concrete cases demonstrate that AI is already significantly improving patient care.
A research laboratory has asked us to improve the detection of skin cancers. Using machine learning, we have developed A cancer cell recognition model, trained on thousands of medical images. Result: faster, more accurate detection, and better support for dermatologists in their decision-making.
In the hospital sector, another project has enabled’preventing readmissions Thanks to the analysis of patient data, AI identifies at-risk profiles and allows doctors to adjust care proactively. This approach reduces the burden on hospitals and improves the quality of treatments.
AI also plays a key role in the optimisation of the patient pathway. Predictive tools make it possible to personalise prevention, direct patients to the right specialists, and improve post-operative monitoring through remote surveillance. In radiology, image analysis algorithms identify anomalies that the human eye might miss, thereby strengthening the reliability of diagnoses.
In the pharmaceutical industry, artificial intelligence is speeding up clinical trials and enabling the identification of the best candidate molecules for a drug. Thanks to AI, innovative treatments are emerging more rapidly and at a lower cost.
Despite these advances, AI remains too often considered a one-off experiment, rather than being integrated in a structured and strategic way into healthcare facilities.
If AI is so promising, why is its adoption still limited in many institutions?
Certains idées reçues freinant son déploiement. Beaucoup perçoivent l'intelligence artificielle comme une technologie trop complexe à intégrer, nécessitant des compétences spécialisées que les équipes internes n'ont pas. D'autres craignent un coût d'investissement trop élevé ou s'inquiètent des enjeux éthiques et réglementaires.
In reality, AI should not be seen as a replacement for existing practices, but as A decision support and optimisation tool. Its integration can be progressive and adapted to the specific needs of each medical facility.
This is where IA Starter, a tried and tested method for structuring an effective and actionable approach.
At JEMS, we have designed IA Starter to enable medical facilities to’Integrate AI in a structured and progressive manner. Our methodology is based on four main stages.
First, a phase acculturation where we raise awareness among medical and administrative teams about the challenges of AI. Understanding its benefits and limitations is the first step towards successful adoption.
Next, we work with our clients to Identify concrete use cases who meet their real needs. No generic solutions, but a tailored approach.
Nous co-construisons ensuite une Pragmatic and achievable roadmap of a few months, in order to guarantee a effective and measurable deployment solutions.
Finally, we assist the implementation and monitoring AI projects, adapting strategies based on feedback and technological advancements.
Our approach is designed to avoid the pitfall of POCs that go nowhere and allow for a smooth and gradual adoption of artificial intelligence.
Artificial intelligence is already transforming medicine. Those who embrace the change today will be the leaders of tomorrow's healthcare.
It allows for improved diagnostic accuracy, optimisation of hospital workflows, acceleration of research, and personalised care. The benefits are considerable, but only a well-thought-out strategy allows for their full exploitation.
At JEMS, we believe tha AI must be an accessible and pragmatic lever, supporting caregivers and patients.
Would you like to find out how to integrate artificial intelligence into your medical establishment or company? Contact us now to discuss your project and define a suitable strategy together.
