Vincent Jacquelinet
CEO
5 minutes
Artificial intelligence is gradually establishing itself in medical practices and healthcare facilities.
Following online appointment booking tools and teleconsultation, a new generation of support is emerging: AI medical assistants.
Their promise is simple: to allow doctors to devote more time to care by automating certain administrative, documentary, or organisational tasks.
Depending on the solution, the medical assistance AI can intervene:
before the consultation;
during the consultation;
or after the consultation.
AI medical assistants are often presented as a single category. In reality, they cater to very different uses: medical documentation, decision support, or preparation of consultations.
This comparison aims to help healthcare professionals understand the differences between the main solutions available in France in 2026.
An overview of five particularly visible players in France in 2026.
Solution | Country | Founded | Funding | When does it intervene? | Primary use |
|---|---|---|---|---|---|
Heidi | 🇦🇺 Australia | 2019 | ~ $100 M | During / after consultation | Listens to the consultation and writes the summaries |
Tandem | 🇸🇪 Sweden | 2023 | ~ $50 M | During / after consultation | Listens to the consultation and writes the summaries |
Doctolib AI Assistant | 🇫🇷 France | 2013 | ~ €800 M | During / after consultation | Medical scribeListens to the consultation and writes the summaries |
MedGPT | 🇫🇷 France | 2017 | ~ €40 M | Before / during / after consultation | Answers medical questions and assists with decision-making |
Aldebaran | 🇫🇷 France | 2022 | ~ €2 M | Before / during / after consultation | Prepares the consultation, collects symptoms, updates medical history, and generates a clinical summary |
1. Heidi
Country: Australia
Created: 2019
Total funding: approx. $100M raised
Heidi is currently one of the world's best-known players in the medical scribe market.
The tool listens to the interaction between the doctor and the patient during the consultation, then automatically generates:
a report;
a clinical note;
or various medical documents.
Value proposition
👉 Reduce the time spent on medical documentation after the consultation.
Strong point
Very high transcription quality and strong international adoption.
Limitation
The tool is primarily active during and after the consultation.
2. Tandem Health
Country: Sweden
Created: 2023
Total funding: approx. $50M raised
Tandem Health is developing an AI medical assistant focused on clinical workflows.
Like Heidi, Tandem relies primarily on listening to consultations and automatically generating medical documentation.
Value proposition
👉 Reduce the doctor's documentation and administrative burden.
Strong point
Particularly polished user experience and strong growth in Europe.
Limitation
The model remains primarily focused on the consultation itself.
3. Doctolib AI Assistant
Country: France
Created: 2013
Total funding: over €800M raised
After transforming online appointment booking in Europe, Doctolib is now investing in clinical AI with its Consultation Assistant.
The tool relies mainly on listening to the consultation to automatically generate a structured medical note and help draft clinical documents.
Value proposition
👉 Reduce the time spent on medical documentation by integrating AI directly into the environment already used by many doctors.
Strong point
A native integration into the Doctolib ecosystem (calendar, patient record, teleconsultation).
Limitation
The approach remains mainly focused on the consultation itself and the production of medical documentation.
4. MedGPT (Synapse Medicine)
Country: France
Creation of Synapse Medicine: 2017
Total funding: approximately €40M raised
Developed by Synapse Medicine, MedGPT leverages the company's historical expertise in medication analysis, prescription assistance, and treatment safety.
Unlike many general-purpose assistants, MedGPT is designed specifically for healthcare professionals and medical use cases.
Value proposition
👉 Provide medical AI capable of answering clinical questions, searching for reliable medical information, analyzing complex therapeutic situations, and supporting professionals in their decision-making.
The company also strongly highlights:
data sovereignty;
hosting in France;
regulatory compliance;
traceability of medical sources;
and the use of verifiable scientific and medical references.
Strong point
Strong medical and pharmaceutical expertise as well as a sovereign positioning particularly valued by healthcare establishments.
Limitation
The product is more oriented towards decision support and medical information retrieval than the operational preparation of consultations.
5. Aldebaran Care
Country: France
Created: 2022
Total funding: €2M raised (for now)
Aldebaran takes a different approach to most AI medical assistants on the market.
While the majority of solutions intervene during or after the consultation, Aldebaran focuses on pre-consultation.
Its digital medical assistant relies on clinical AI based on scientific evidence to prepare the consultation before the patient even arrives at the office.
Concretely, the tool can:
collect symptoms;
update history;
request relevant documents;
calculate certain clinical scores;
identify certain preventive actions;
or generate a structured medical summary for the doctor.
Value proposition
👉 Free up medical time by performing part of the medical history taking and information collection before the consultation, allowing the doctor to focus more on clinical reasoning and medical decision-making.
Strong point
An approach centered on clinical organization, consultation preparation, and structuring medical data prior to the appointment.
Limitation
Like any pre-consultation solution, its effectiveness depends on the patient's commitment to preparing for their appointment and answering the proposed questionnaires.
Do all AI medical assistants pursue the same goal?
Not really. Today, three main approaches are emerging.
Consultation assistants
They intervene during or after the appointment.
Their main goal is to:
reduce documentation;
automate reports;
limit re-entry.
This is particularly the case with Heidi, Tandem, or the Doctolib AI Assistant.
Decision support assistants
They intervene during the consultation when the doctor is looking for medical, therapeutic, or regulatory information.
Their goal is to:
facilitate access to medical knowledge;
secure certain decisions;
or quickly find reliable information.
This is notably the approach developed by MedGPT.
Pre-consultation assistants
They intervene before the appointment.
Their goal is to:
better prepare the patient;
structure medical information;
retrieve useful documents;
update medical history;
and allow the doctor to reach clinical reasoning more quickly.
This is notably the approach developed by Aldebaran.
Conclusion
AI medical assistants probably represent one of the most significant developments in the organisation of consultations since the widespread adoption of online appointment booking.
While early uses focused on document automation, new approaches are now emerging around medical decision support and consultation preparation.
The goal is not to replace the doctor.
The goal is to enable them to dedicate more time to what creates the most value: listening, clinical reasoning, and medical decision-making.
