Over the past few years, several AI initiatives have been developed by academia and industry, especially in the life sciences and health technologies (LSHT) sector. The Quebec Research Funds (QRF), Montreal InVivo, and other organizations have been closely following the acceleration of innovations in this area and have worked together to align them.

With the publication of the first national AI strategy in the world (CIFAR, 2017) and the presence of pioneering scientists in the field, Canada is renown as one of world leaders in AI. Currently ranked 4th out of 62 countries by the Global AI Index (Tortoise, 2019), Canada intends to reinforce its position by developing AI in the LSHT sector. More specifically, Quebec and the Greater Montreal region are gaining credibility in terms of AI advances applied to health. The many scientists, research laboratories and industries/companies in the region make it a prominent place to develop and implement AI in the LSHT sector. The interest for AI in Quebec was recently demonstrated by several memoirs submitted during the consultations for the 2022 Quebec Research and Innovation Strategy.

When it is associated with innovations in the LSHT sector and deployed responsibly, AI can increase the efficacy and security of care and services of the health network but also make them more accessible. Furthermore, AI-integrated tools are useful to prevent the spread of diseases, in addition to helping the development and discovery of new treatments and diagnostic tools (CIFAR, 2020).

Context and objectives

In the context of its strategic committee for AI in LSHT, Montreal InVivo mandated Accenture in 2019 to conduct a strategic analysis of the sector. Simultaneously, Montreal International mandated EY to analyse Montreal’s positioning in relation to the other main AI-health hubs elsewhere in the world. Following the combination of the recommendations from their respective studies, Montreal InVivo and Montreal International published a white paper with five major recommendations for the development of the sector of AI-health sector in the Greater Montreal area. This work served as a basis to orient the creation of an AI in health innovation zone, co-led by both organizations and for which a business plan was submitted to the Quebec Ministry of Economy and Innovation in January 2021.

All of these initiatives eventually set the stage to develop a map of the recommendations for the development of AI in the LSHT sector. The joint recommendations from Montreal InVivo and Montreal International echo those voiced by several other organizations operating across Canada to enhance the development of the AI sector. The main recommendations were identified for the themes found in the many documents used as a basis for this map. The goal of this exercise is to provide a broad, but precise, overview of the situation to the decision-makers, based on the independent findings of different organizations.

List of the main themes identified in the documents selected for the analysis

Shorts definitions of the themes

Collaboration between stakeholders : Addresses how to strengthen or implement collaborations between stakeholders.

Commercialization/ implementation in the healthcare system : Addresses recommendations regarding commercialization and/or implementation in the healthcare system.

Data – Computer equipment : Addresses requirements in terms of computer equipment.

Data – Cybersecurity : Addresses the recommendations about cybersecurity.

Data – Digital infrastructure/data management : Addresses the necessary digital infrastructure and the recommended approaches in data management.

Data – Global theme : The topic of data in a general context.

Data – Legal framework : Addresses the necessary changes in the legislative framework around data access and processing.

Environmental impact : Addresses the environmental impacts of AI in LSHT.

Funding : Addresses funding requirements.

General and continuing education : Addresses the general and continuing education to be developed.

Government support : Addresses the government support to be strengthened or implemented.

Responsible approach – Citizen acceptability : Addresses recommendations related to AI citizen acceptability and how to strengthen it.

Responsible approach – Ethics : Addresses the ethical aspects to be considered.

Responsible approach – Inclusion and diversity : Addresses recommendations related to inclusion and diversity.

Here is an overview of the major findings from the 24 documents listed in the Appendix :

  • The development of AI in LSHT would not be possible without collaboration between stakeholders, nor without international collaboration. It is important to create and enhance opportunities for stakeholders to discuss and exchange around various topics. Note that the importance of collaboration for the development of AI was pointed out in each of the 24 documents chosen for this analysis. AI touches upon many scientific, social, legal, and ethical issues. Stakeholders should meet to collaborate and create secure, value-added tools that respect the concerns of every stakeholder. Their collaboration is important for the latter to have opportunities to discuss a variety of topics, such as the sharing of expertise, good practices, AI governance systems, collaborative research projects, co-design approaches with patients and professionals, as well as the societal impact of AI.
  • The need to modernize and standardize the federal and provincial regulation for access to health data was identified as a priority for the development and implementation of AI in LSHT. In particular, the timeframe to access data for research and innovation purposes should be shortened to improve health care and services. This will also facilitate interoperability between data and information systems while maintaining a high level of security and confidentiality.
  • The development and implementation of AI in LSHT requires 2 types of support. First, government support will be essential, especially in terms of legislative framework, education at the citizen level and the choice of Canada as a destination for top talent in the field. Second, adequate funding will be crucial to the development and implementation of AI, especially when it comes to investing in the development of start-ups and companies and to supporting the creation of jobs and task forces, such as Quebec’s Table nationale des directeurs de recherche.
  • To increase the integration of innovations, to test them in real-world settings and to measure their value, it will be essential to develop the healthcare system’s agility by investing in the development of skills and by highlighting the importance of building a culture based on information and measurement.
  • It is necessary to develop a critical mass of skilled people in AI in health by attracting, educating, and retaining them on the territory. Continuing education of people already in the workforce is also essential to promote the implementation of AI in health.
  • The development of AI in LSHT should be conducted in a transparent and responsible manner, by including citizens at every step of the innovation cycle. The importance of consolidating monitoring activities for the AI in LSHT sector was also frequently mentioned in the consulted documents.

This map highlights that the major themes can be found in all the studied documents, indicating an alignment of the different stakeholders in terms of the necessary next steps to develop this sector. Certain themes were only infrequently touched upon, especially those related to the integration of Indigenous people in the development process and to the consideration of environmental aspects. Another less-frequently mentioned theme was the necessity to digitalize the healthcare network as well as a country-wide access to the digital storage and analysis infrastructure necessary for the development of these innovations. To position Canada and the Quebec region as the world leader in AI in the LSHT sector, the time has come for action and for the execution of these frequently repeated recommendations by key stakeholders in the sector.

Numbers of the total documents (24)


1 - Mémoire – Projet de loi 64 : Accès aux mégadonnées de santé (in French)

2 - Recommandations sur l'optimisation des processus d'accès aux données de santé en vue du budget provincial 2020-2021 (in French)

3 - Task force report on artificial intelligence and emerging digital technologies

Autor(s) : Royal college of physicians and surgeons of Canada

To learn more, click here.

4 - Health data protection law in the era of big data : risks and opportunities for modernization (access with a fee)

Autor(s): Various authors & Université de Toronto

5 - Do no harm: a roadmap for responsible machine learning for health care (access with a fee)

Autor(s) : Wiens et al., Universities of the United-States and Canada

To learn more, click here.

6 - Montreal declaration for a responsible development of AI

7 - Recommandations découlant des consultations sur les modalités d'intégration des innovations technologiques dans le RSSS (in French)

8 - AI for public health equity

9 - White paper on AI in LSHT

10 - Building a learning health system for Canadians

11 - Data Trusts : a new tool for data governance

12 - Report of Canada's economic strategy tables : health and biosciences

Autor(s) : Canada’s economic strategy tables

13 - Trustworthy AI in health

Autor(s) : Organisation for Economic Co-operation and Development (OECD)

14 - AI on a social mission: Recommendations 2018 – Part 1 AI governance and policy

Autor : AI on a social mission

To learn more, click here.


15 - Integrating robotics, AI and 3D printing technologies into Canada's healthcare system

Autor(s) : Standing senate committee on social affairs, science and technology

16 - Indigenous protocol and artificial intelligence

Autor(s) : Indigenous protocol & artificial intelligence working group

17 - Call to action for a responsible innovation in digital health

18 - G7-Science academies statement 2020 : Digital health and the learning health system

19 - A jurisdictional scan of global health data resources (restricted use)

20 - Principes directeurs pour assurer le fonctionnement et la gestion optimale d'un centre d'accès aux données de santé (in French)

Autor(s) : Table nationale des directeurs de la recherche, governance and management subgroup

21 - Application of AI approaches to tackle public health challenges

22 - AI on a social mission : Recommendations 2018 – Part 2 The role of education in the implementation of AI (in French)

Autor : AI on a social mission

To learn more, click here.

23 - Place de l'IA dans les professions : enjeux pour la formation collégiale (in French)

Autor : Groupe DDM

To learn more, click here.

24 - Adequacy diagnosis Training-Skills-Employment

Autor(s) : Montréal InVivo, Conseil emploi métropole & Pharmabio