Clinical decision support in mental healthcare, powered by AI.

Aifred uses AI to accomplish what was previously not possible with other methods: helping clinicians and patients make better treatment choices as they are faced with a large number of treatments options that are all similarly effective at the population level, but not at the individual patient level. Along with a network of domain experts in psychiatry, we are harnessing the power of AI to identify patterns in patient data that can predict optimal personal treatment.

Aifred is also innovating in the area of clinical interpretability, to reduce the ‘black box’ nature of deep learning models and to provide physicians and patients with the rationale for the AI’s treatment recommendation.

Quality of model predictions are very important and we analyze this carefully, making sure to take only the best models to clinical practice. By training our predictive models on high-quality and reliable clinical datasets, we ensure quality input to our model. De-identified patient outcomes collected by our tool over time will feed back into our neural networks to continuously improve Aifred’s predictive power.

Our solution uses AI to learn from the clinical data of thousands of patients to help tailor individual treatment, significantly reducing the time it takes for a patient to reach remission. Specifically, we use deep learning to perform differential treatment selection, which allows us to capture complex relationships within patient data. We have built a clinician-patient application that allows patients to answer questionnaires about their mental state and quality of life, and visualizes this data for both the patient and clinician while providing key decision support for treatment.

Fundamental

Our team is conducting leading research into new interpretability techniques and into further improving our differential treatment benefit prediction. Our researchers have also conducted in-depth research to identify predictors of treatment response and side effect burden in depression including examining the potential feasibility of adding biomarker testing in routine clinical practice, although our tool is not dependent on such input today.

Clinical

Clinical research is focused on validating our model in controlled and real-world conditions. We are running pilots of our current product at three hospital sites in Canada and have also tested our product with the AI included, in a simulation center environment. Now we are beginning the first ever clinical studies of an AI-powered clinical decision support system in depression that does not rely on inputs from additional tests. We are blazing the trail when it comes to clinical validation of deep-learning based clinical decision aids, and as such are investing heavily in the development of ethical principles to guide development and testing and in ensuring safety and useability of our tools. In fact, ethical development is so important to us that we have created our own ethical framework, known as Meticulous Transparency, to guide our work.