AI for Mental Healthcare

The complexity of AI models makes it hard to operationalize AI. Both therapists and patients often have concerns about how an AI model gets to recommendations. It is then important that model decisions are fully transparent and explainable, in order to operationalize these important innovations.

Learn more about how health organizations, such as NiceDay, have used Deeploy to implement AI in mental health therapy with trust, efficiency, and compliance.

Healthcare organizations using Deeploy

Establish trust with therapists & patients

Ensure transparency

The complexity of AI models creates distrust among therapists and patients, who might have concerns about how an AI model gets to a certain recommendation. It is then important that model decisions are fully transparent and explainable.

When deploying a model on Deeploy, data teams can do so using a variety of explainability techniques as well as custom explainers. This ensures that every AI recommendation the therapist receives is accompanied by the why behind that recommendation.

Which general trends are represented by the model? Which feature contributed most to the prediction? What input would lead to a different decision?

Keep medical experts in the lead

Besides educating therapists on the underlying reasons behind the recommendations, it is crucial that they can correct model decisions if necessary, ensuring that medical experts stay in the lead of decision-making.

The answer to this is implementing a feedback loop, a process that allows therapists to evaluate how correct model decisions were. After being presented with a recommendation, the therapist can either accept it or overrule it and give feedback on this overruling. This preserves the human touch in mental healthcare while elevating it with the power of AI.

The evaluations from therapists can then be translated onto Deeploy, where data teams can monitor the percentage of overruled recommendations trough the disagreement ratio metric. This helps determine model effectiveness and identify areas for improvement. It is also possible to set alerts for the disagreement ratio, meaning teams get notified when the disagreement ratio gets too high, which can inform when to perform a model update.

Minimize risks & stay in control

Given the ethical and regulatory risks of integrating AI into mental healthcare, strict control and oversight are vital. This ensures organizations can quickly address any adverse developments.

Proactively ensure oversight & accountability

Ensuring oversight and clear ownership over running models leads to better AI governance. Deeploy enables teams to deploy, serve, monitor, and manage AI models in one single platform, allowing for easier oversight while saving time and resources.

Teams and workspaces help ensure the right oversight and access to different AI applications within the same organization. Moreover, it is also possible to assign model owners to each model, who are responsible for maintaining control over its ongoing operation.

Monitor performance & act quickly

AI applications must be solid and trustworthy and continue to perform well throughout their lifecycle. To achieve this models must be monitored over time for traffic, errors, performance, and drift, among other metrics.

Deeploy combines all necessary monitoring metrics into the same platform, ensuring strict model monitoring. Moreover, alerts can also be set for all monitoring metrics, allowing teams swift action in case of model degradation.

Comply with legal requirements

Healthcare organizations must comply with regulatory requirements like the EU AI Act, and the Medical Device Regulation (MDR) to ensure patient protection when deploying AI solutions. Namely, AI systems that qualify as medical devices will be defined as high-risk under the soon-enforced EU AI Act

Maintain transparent operations

It is vital for regulatory compliance that AI systems operate transparently. As mentioned, Deeploy ensures transparency and explainability of all model recommendations. Moreover, all recommendations and their accompanied explanations can be traced back and reproduced, creating a full audit trail over model decisions and aiding in pinpointing any issues.

Keep model documentation & records readily available

Model documentation & record keeping are important regulatory requirements for AI applications. Organizations must ensure that information regarding each AI application is easily available and that all events around the deployment are logged.

Deeploy facilitates these requirements by allowing teams to create and store model cards, where information on model capabilities, intended use, performance characteristics and limitations can be stored. Additionally, all events for every deployment are automatically recorded and stored, creating a full record that can be consulted when needed.

Stay on top of compliance processes

To ease compliance, teams should also be able to keep track of progress in fulfilling requirements. Deeploy offers both standard and customizable compliance templates, enabling teams to verify that compliance requirements are fulfilled for every AI application within the organization.

While standard templates offer general guidance on high-level regulation, custom compliance templates allow teams to upload checklists tailored to fit any specific  requirements that might be important to the organization.

How to get started with Deeploy

Would you like to learn more about how you can take your first steps with Deeploy? Let one of our experts walk you through the platform and how it can be leveraged for your specific concerns or start with a trial of our SaaS solution.