Artificial Intelligence

AI plays a role in drug discovery. There is a wealth of existing or collectable data to better understand diseases. This information also makes it possible to develop drugs which can increasingly be personalized to the patient’s profile. The information collected makes it possible to create 3D models, “design” and test a molecule virtually. In addition, some of these technologies allow the analysis of a patient’s profile to determine the patients who will best respond to the new drugs.

The functional and structural bioinformatics research unit, headed by Sébastien Lemieux, aims to pre-process the data in order to make it easier to use and subsequently extract meaningful information. The group is particularly interested in the level of RNA message expression used to identify splicing events or the presence of mutations.

MIMS and Perceiv AI companies participate for their part in helping to design clinical studies. Using their various algorithms, they are able to find the population that could best respond to the medication being tested and show its effectiveness. They are able to create patient subpopulations based on their susceptibility to react well to the drug or to develop complications.

Other platforms focus on testing candidate molecules, predicting drug metabolism, checking their effectiveness at targeted sites, and determining possible interactions with other drugs. InVivo AI et Molecular Forcaster companies offer platforms of this type. InVivo Ai focuses more on the development of a therapeutic agent for diseases with a genetic origin with little information such as certain rare diseases or cancers. Molecular Forcaster for its part is developing the platform for medical discovery experts.

The various information gathered about a patient allows doctors to make a diagnosis. With the advancement of technology, the use of imagery is becoming more important to assist in decision making. Image analysis can sometimes be complex and that’s when AI comes in to help. This is one example where AI is used to aid in the diagnosis of disease. Here are other examples of using AI.

Artic Fox AI, for its part, extracts information from the magnetic resonances of the brain in order to make diagnoses of neurodegenerative diseases such as Alzheimer’s.

In collaboration with partners from the hospital, biotechnology, pharmaceutical and medical equipment sectors, Imagia uses the full potential of all organizational data to achieve medical breakthroughs. Imagia scientists use a process called radiomics to find biomarkers in patient imagery data. They are developing data processing systems that will be able to analyze these images and thus predict the progression of a disease as well as the patient’s response to treatment.

Airfred Health put on the market a product that helps psychiatrists choose the best treatment for a patient with mental illness. Thanks to an analysis of a patient’s information, the platform makes it possible to predict the patient’s reaction to treatment and to follow the patient during treatment.

The information obtained by analyzing the eye and its movements is of great interest to various SMEs which use it in the diagnosis of diseases such as concussions, vestibular diseases, neurological diseases or ophthalmological diseases. SMEs such as Saccade Analytiques, Zilia, Optina et Diagnos use all AI related to eye analysis.

One of the priorities in Canada and Quebec is to optimize health care. Access to specialists is a huge issue, especially for patients in remote areas. In addition, increasingly personalized treatments require an optimization of access to diagnosis and monitoring. Innovations like telemedicine and remote patient data access through artificial intelligence can help. In addition, connected objects will be able to generate a large number of data accessible remotely, often continuously, to optimize the monitoring carried out in telemedicine. Artificial intelligence will play an important role in helping the specialist navigate through this impressive amount of data in order to draw the right conclusions. Here are examples of initiatives that play a role in optimizing health care and its access.

The AlayaCare platform allows patients to have access to a home doctor. Through a video conference system, the patient can chat with his doctor. In addition, the platform allows home care teams to collect relevant information about the patient’s health or even to mobilize the patient to enter certain information himself. Using this data, the AlayaLab platform analyzes the results to predict and prevent harmful events for the patient.

Tactio is a leading provider of connected health solutions allowing patients, healthcare professionals and businesses to digitize patient-centered care paths with FHIR / SMART compatible solutions integrating mobile, web, IoT , interoperability of health systems, cloud computing technology and artificial intelligence.

Hexoskin is a Montreal-based company that develops biometric clothing that monitors patients on a daily basis and collects a huge amount of medical data. These garments are used in several spheres of health such as cardiology, pneumology, neurology, psychiatry or pediatrics. This data, placed in a connected health platform, can be used for research and as a communication tool for nursing staff.

Moonshot Health creates a new healthcare model that goes beyond episodic office medicine to provide preventive, continuous and collaborative care using digital biomarkers. The company is implementing a platform for the transparent collection of health-relevant information to detect changes that may signal the onset of illness or the need to change behavior or medical intervention. The platform connects to many data sources (portable devices, connected devices, electronic medical records, social platforms, etc.) and uses AI to develop and validate “digital biomarkers”, real health indicators to use for the detection and monitoring of disease states.

The use of data by artificial intelligence can be done in different ways, whether by deep learning which allows the machine to create reasoning from the data it analyzes until the automatic processing of natural language which allows us to understand human language. These various methods require a range of programming and research. However, the use of data collected from patients for the purpose of analysis or the creation of artificial reasoning raises a large number of questions regarding confidentiality and ethics. In Montreal, organizations working in artificial intelligence have decided to create a declaration laying the foundations of ethics for this technology.

On November 3, 2017, the Université de Montréal launched the co-construction of the Montreal Declaration for the Responsible Development of Artificial Intelligence (Montreal Declaration). Montreal Declaration is a collective work that aims to put the development of AI at the service of everyone’s well-being, and to guide social change by developing recommendations with strong democratic legitimacy.

Montreal is recognized as an important pool for AI. The research carried out there is of great influence for the advancement of AI. in 2018, Yoshua Bengio, a pioneer in deep learning, received the Turning Prize, “the Nobel Prize in computing”. Professor at the University of Montreal, he is a world reference in artificial intelligence and one of the most cited computer scientists in 2018. He is the founder of the Quebec Institute of Artificial Intelligence, MILA, of which he is the scientific director.

Mila is the result of a collaboration between the Université de Montréal and McGill University, in close collaboration with the École Polytechnique de Montréal and HEC Montréal. Mila brings together researchers specializing in the field of deep and reinforcement learning. Recognized worldwide for his significant contributions to the field of deep learning, Mila has particularly distinguished itself in the fields of language modeling, machine translation, object recognition and generative models. Mila is creating a unique space for innovation in artificial intelligence and technology transfer that will take advantage of interactions with industry and encourage the emergence of start-ups while integrating the social impacts of technologies in its projects.

The laboratory of Doina Precup, member of MILA, stands out for its activities. A professor at McGill University, Doina Precup focuses his basic research on reinforcement learning, including applications of artificial intelligence in areas of social impact such as health care. She is interested in making machine decisions in situations with a high degree of uncertainty.

The big technology companies have chosen Montreal to open their AI laboratories:

Cutting-edge research laboratory, Microsoft Research Montréal

makes an important contribution to studies aimed at teaching machines to read, think and communicate like human beings. The laboratory focuses on machine understanding, dialogue and machine reinforcement learning.

DeepMind is an artificial intelligence company acquired by Google in 2014. The company draws on neuroscience and develops general-purpose learning algorithms for use in solving some of the world’s most pressing issues

FAIR (Facebook Artificial Intelligence Research) is an organization of several artificial intelligence research laboratories linked to the Facebook company. FAIR works in partnership with the Canadian Institute for Advanced Research, the Montreal Institute for Learning Algorithms (MILA), McGill University and the University of Montreal.

Samsung Advanced Institute of Technology (SAIT) is Samsung’s research and development center. Incubator of advanced technologies, its philosophy of “unlimited research” contributes to unprecedented scientific breakthroughs. The SAIT AI Lab Montreal plays a key role within the group because of its research in artificial intelligence and deep learning.

Besides MILA, other establishments are recognized for their expertise in artificial intelligence.

The school of artificial intelligence in healthcare of the CHUM is a world first. It focuses on the development of human capacities and the implementation of artificial intelligence in a real environment. It allows its community to develop, to apply artificial intelligence to health and to measure the impacts for patients, teams, the health system as well as to extend knowledge and skills to internationally.

The Institute for data valorization (IVADO) aims to bring together industry professionals and university researchers to develop cutting-edge expertise in the fields of data science, optimization (operational research) and artificial intelligence. The members of IVADO propose methods to process information and thus favor decisions optimizing the use of resources. Concretely, IVADO encourages exchanges and knowledge sharing between specialists, partners, researchers and students in its network.

The Montreal Center for Higher Education in Artificial Intelligence (PIA) aims to mobilize educational institutions to align the talents of artificial intelligence. The PIA aims to increase the capacity of CEGEPs and universities in Montreal to rapidly develop the offer of higher education in artificial intelligence in collaboration with interested partners. It wants to promote the transfer of available expertise for the socio-economic development of Montreal and Quebec through organizations that are – or want to become – stakeholders in the development of artificial intelligence.

If you wish to develop your skills and knowledge in Artificial Intelligence, check out the list of available training catalogs:

• Digital Health Consortium of the University of Montreal
• IVADO
• EAIS
• Directory of training programs of Cégeps and University of Canada

To know your level of knowledge, evaluate your knowledge:

• AutodIAg by IVADO
• Self and skills in terms of data by EAIS (French only)

In addition to private companies and public institutions, the ecosystem includes several structuring organizations, supporting start-up companies and partnerships in artificial intelligence. Some organizations work in the business accelerator sector, they offer support programs for start-up companies or even for innovative ideas. They offer mentoring as well as advice to develop a business model:

• The CENTECH powered by the École de technologie supérieure (ÉTS) offers a program for young artificial intelligence entrepreneurs. Since January 2019, CENTECH has partnered with Thales to train AI @ CENTECH and to support and promote projects carried out by start-up companies in the field of artificial intelligence. It accompanies and supports new ideas in artificial intelligence in order to lead them to marketing.
• CTS Santé specializes in medical technologies. It provides coaching to start-up companies who already own their business model and helps them obtain financing for the commercialization of their model.
• District 3 is affiliated with Concordia University and supports innovative ideas through to the production of prototypes. It forms a network of mentors to help ideas emerge.

Other structuring organizations are working to rally industry and public institutions in order to move from research to development and commercialization more quickly and easily :

• The two consortia of the LSHT sector, the industrial research consortium and the research and innovations consortium in industrial bioprocesses MEDTEQ, PROMPT, CQDM et CRIBIQ support researchers and their innovative projects using artificial intelligence in health to obtain the necessary grants. These consortia supervise their members so that they progress in the development of their ideas and have easier to establish public-private partnerships. In 2019, PARTENAR-IA, the first concerted call for projects in artificial intelligence, was launched in order to deploy funding in targeted industrial sectors.
• The Discovery and Digital Health Platform (DHDP) of the Terry Fox Research Institute (TFRI) and Imagia aims to create a world-class innovation framework to facilitate collaborations between the health sciences and the sectors of artificial intelligence across the country. The project will advance research aimed at developing precision medical treatments and developing new remedies. The PDSN’s artificial intelligence tools will deliver more personalized care across Canada, improving outcomes and reducing costs for Canadians. This project will put Canada at the forefront of international efforts to accelerate the fight against cancer and other diseases.
• CARTaGENE is a public research platform of CHU Sainte-Justine aiming to accelerate research and innovation and to be a decision-making aid tool while reducing the costs of health research. CARTaGENE is made up of both biological samples and data on the health and lifestyle of 43,000 Quebecers between 40 and 69 years old.
• The CHUM’s Centre d’intégration et d’analyse des données médicales (CITADEL) aims on the one hand to integrate the information necessary to improve patient care and increase the performance of the care system and on the other hand to provide easy, secure, appropriate and timely access to clinical and administrative data CHUM to promote and facilitate research, evaluation and innovation, as well as support for data-driven decision-making.