Accelerating clinical trial start-up with predictive site selection AI - ON-1121

Genre de projet: Recherche
Discipline(s) souhaitée(s): Médecine, Sciences de la vie, Informatique, Sciences mathématiques
Entreprise: Banting AI
Durée du projet: Flexible
Date souhaitée de début: Dès que possible
Langue exigée: Flexible
Emplacement(s): ON, Canada
Nombre de postes: 1
Niveau de scolarité désiré: MaîtriseDoctoratRecherche postdoctorale
Ouvert aux candidatures de personnes inscrites à un établissement à l’extérieur du Canada: Yes

Au sujet de l’entreprise: 

At Banting AI, we’re dedicated to making clinical trials faster, simpler, and more accessible. We use advanced artificial intelligence to streamline clinical research, helping sponsors and sites accelerate timelines, reduce costs, and deliver innovative treatments to patients more effectively. Founded in Canada, Banting AI is committed to improving healthcare outcomes at home and abroad.

Veuillez décrire le projet.: 

Clinical trial initiation remains a major bottleneck, marked by fragmented processes, slow startup timelines, and inconsistent site performance. In Canada specifically, sponsors frequently face difficulties rapidly identifying high-performing research sites, sites encounter operational inefficiencies and unclear trial visibility, and patients experience barriers in accessing suitable clinical trials.

This project aims to address these systemic challenges by developing predictive analytics models leveraging historical operational data, site performance metrics, patient demographics, and Canadian-specific infrastructure elements—including contracting, feasibility assessments, and regulatory approval timelines.

The objectives include:
• Proactively identifying optimal study-site alignments.
• Accelerating trial startup timelines through predictive insights into site performance and operational bottlenecks.
• Improving resource allocation and reducing administrative burdens at clinical research sites.
• Enhancing patient access, especially in underserved, rural, and remote communities, aligning closely with Canadian healthcare equity goals.

Outcomes include:
• Robust predictive AI tools for intelligent and rapid site selection.
• Evidence-based guidelines and best practices for optimizing site initiation processes.
• Tangible improvements in patient accessibility and clinical trial participation.
• Peer-reviewed publications and validated methodologies that are directly scalable beyond Canada to global healthcare markets.

Expertise ou compétences exigées: 

• Predictive modeling and statistical analysis.
• Practical experience with machine learning frameworks (e.g., TensorFlow, PyTorch).
• Familiarity with clinical trial protocols, operations, and key performance metrics.
• Data management expertise (structured/unstructured clinical datasets).
• Strong collaborative and communication skills.
• Understanding of Canadian healthcare regulations, data governance, and privacy standards (PIPEDA).