Over the past decades, many advancements have been made in understanding and treating mental health problems. Next to medicinal and cognitive-behavioral treatments, new forms of treatments are being developed that make use of AI methodologies, such as within Internet-based cognitive behavioural therapies and patient monitoring systems. Such new approaches are potentially able to scale in terms of reach while keeping treatment costs low. However, there are still many challenging problems in their application in practice.
The first key challenge is collecting large, diverse, and high-quality datasets for each specific medical purpose, which can be costly and sometimes impossible. Luckily, recent advancements in AI such as transfer learning, meta learning, and self-supervised learning, will provide us with new ways to overcome this problem. Another key challenge is explainability: if doctors or psychologists cannot explain the predictions made by a machine learning model, they may be less likely to trust and use it.
Another interesting and less-investigated challenge is prediction confidence or uncertainty: the mental health professionals need to understand when a model is certain about a prediction, or when the model is unsure about it. For instance, certain observations might be very dissimilar to observations used during training which as a result decreases model performance. Being able to quantify prediction uncertainty offers a valuable metric in addition to prediction.
Lastly, we need to be aware of bias, which is a well-known problem in AI. It is crucial to be able to measure how biased a trained model is to responsibly use the model. It is clear that AI has an increasingly fundamental role to play in treating mental health disorders, however, for AI to reach its potential, we need to investigate how we can overcome these challenges.
We encourage researchers to submit manuscripts related to these challenges. We appreciate that the interdisciplinary field of psychology, computer science, and AI is still quite new. Therefore, many different aspects of these challenges can be investigated, which can be fundamental or practical.
Such challenges can include:
- Develop and/or apply methods to address the difficulty of collecting large-scale datasets for AI models in practice, relying on alternative approaches such as self-supervised and transfer learning.
- Develop and/or apply methods that can shed light on the decision making process in AI models, providing interpretable explanations for their prediction in the context of medical treatment.
- Develop and/or apply methods to quantify uncertainty in prediction to provide model confidence in addition to its prediction.
- Develop and/or apply methods to measure and address different aspects of bias such as algorithmic and dataset bias.
- Describe examples of how AI has been applied in real life mental health care settings, including both challenges still to be overcome and successes.
Over the past decades, many advancements have been made in understanding and treating mental health problems. Next to medicinal and cognitive-behavioral treatments, new forms of treatments are being developed that make use of AI methodologies, such as within Internet-based cognitive behavioural therapies and patient monitoring systems. Such new approaches are potentially able to scale in terms of reach while keeping treatment costs low. However, there are still many challenging problems in their application in practice.
The first key challenge is collecting large, diverse, and high-quality datasets for each specific medical purpose, which can be costly and sometimes impossible. Luckily, recent advancements in AI such as transfer learning, meta learning, and self-supervised learning, will provide us with new ways to overcome this problem. Another key challenge is explainability: if doctors or psychologists cannot explain the predictions made by a machine learning model, they may be less likely to trust and use it.
Another interesting and less-investigated challenge is prediction confidence or uncertainty: the mental health professionals need to understand when a model is certain about a prediction, or when the model is unsure about it. For instance, certain observations might be very dissimilar to observations used during training which as a result decreases model performance. Being able to quantify prediction uncertainty offers a valuable metric in addition to prediction.
Lastly, we need to be aware of bias, which is a well-known problem in AI. It is crucial to be able to measure how biased a trained model is to responsibly use the model. It is clear that AI has an increasingly fundamental role to play in treating mental health disorders, however, for AI to reach its potential, we need to investigate how we can overcome these challenges.
We encourage researchers to submit manuscripts related to these challenges. We appreciate that the interdisciplinary field of psychology, computer science, and AI is still quite new. Therefore, many different aspects of these challenges can be investigated, which can be fundamental or practical.
Such challenges can include:
- Develop and/or apply methods to address the difficulty of collecting large-scale datasets for AI models in practice, relying on alternative approaches such as self-supervised and transfer learning.
- Develop and/or apply methods that can shed light on the decision making process in AI models, providing interpretable explanations for their prediction in the context of medical treatment.
- Develop and/or apply methods to quantify uncertainty in prediction to provide model confidence in addition to its prediction.
- Develop and/or apply methods to measure and address different aspects of bias such as algorithmic and dataset bias.
- Describe examples of how AI has been applied in real life mental health care settings, including both challenges still to be overcome and successes.
