Published on 04/25/25
AI and Health - Opportunities, risks and best practices
AI in healthcare, between promise and constraints
Imagine a future where artificial intelligence transforms the medical sector, with more accurate diagnoses, more effective treatments and smoother access to information. A future that gives every patient a better chance of recovery. It sounds crazy, but it's not science fiction - it's our near future. Nevertheless, great caution is called for, because in medicine, a simple error can have dramatic consequences. This is why the integration of AI must be rigorously supervised, to avoid any risk of bias or erroneous decisions. Lives depend on it.
How can we ensure a strict regulatory and ethical framework?
For AI to be confidently adopted in the healthcare sector, it's crucial to put in place a robust safety and ethical framework. Protecting patients and guaranteeing reliable results being the top priority.
- Healthcare data hosting: to comply with French regulations, companies must use HDS servers (Hébergeurs de Données de Santé), certified by the French Ministry of Solidarity and Health.
- RGPD (General Data Protection Regulation): Medical data must be collected, stored and processed in compliance with the RGPD, guaranteeing transparency and patient safety.
- Consent and anonymization: Explicit patient consent is often required, and in some cases this data must be anonymized or pseudonymized to avoid identification in the event of a leak.
- The AI Act and AI regulations: The European Union imposes strict regulations on high-risk AI. For example, it is forbidden to use systems that manipulate human behavior or exploit psychological flaws. AI solutions in healthcare must therefore be supervised by humans, and a fortiori by doctors, and their mode of operation must be perfectly transparent.
How can AI be used in healthcare?
An advanced search engine in a data library
The strength and power of AI lies in its ability to browse, sort and cross-reference thousands of medical contents instantaneously. This makes it the ideal tool for providing doctors and patients alike with the information they need, effortlessly and relevantly.
👉 Case in point: We developed a chatbot to explore a catalog of oncology articles. This tool highlights the most suitable articles based on the questions asked, without interpretation, but enriching the contextual search.
Appointment scheduling and management
Now consider an AI capable of managing your appointments on Doctolib. All you'd have to do is enter your constraints (schedules, availability, emergencies) and the AI would suggest the best time-saving options. This use case is already a reality, and will become increasingly popular.
AI-based training for healthcare professionals
An AI-based training model could simulate conversations between a healthcare professional and a fake patient. This exercise would enable them to perfect their diagnostic research, listening skills and ability to manage complex situations through vocal training with an intelligent conversational agent.
Clinical decision support
What if an AI could compose a dashboard for you containing raw medical information, enriched with relevant suggestions and analyses? Thanks to this pragmatic, global approach, doctors would have everything they need to make informed, balanced and transparent decisions.
Facilitating administrative tasks
AI is capable of generating pre-filled reports from a consultation, which the doctor would then simply have to rework. This saves valuable time and significantly improves the quality of medical records. And we're not just talking about the handwriting of our dear doctors, but also the information contained in the file. AI forgets nothing, omits nothing, and transcribes data with total fidelity.
What are good technical practices?
To ensure the safe and effective integration of AI, certain best practices need to be put in place:
- Hosting and security: Models must be deployed on secure infrastructures that comply with cybersecurity standards and regulations.
- Access control: Unauthorized access to medical data must be prevented. Strict protocols must govern their use, and ensure that medical confidentiality is preserved.
- Mandatory human validation: All recommendations derived from an AI model must be validated by a healthcare professional before being applied.
Where to start?
The adoption of AI in healthcare must be guided by a clear strategy and a strict framework. Here are three essential steps for effective implementation:
- Clearly define the objectives of AI in your organization.
- Evaluate the level of acceptable risk according to the intended uses.
- Rely on a reliable partner to develop an appropriate solution. Why not Arneo? 😉
Finally, we would like to remind you that AI, however powerful, is a tool. It will never replace the experience and intuition of the medical profession, but like all tools, it can improve results, facilitate access to information, simplify tasks and, above all, save precious time. Like the invention of the wheel or electricity, it marks a major and inescapable change in our habits.