# Applying Artificial Intelligence to Clinical Decision Support in Mental Health: What Have We Learned?

arXiv: https://arxiv.org/abs/2303.03511 · HTML: https://mlsanity.com/paper/2303.03511/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 61 references that are in the map.
- First posted on arXiv 6 Mar 2023.

## Builds on (most cited first, 2 of 2)

- [Interpretability of machine learning based prediction models in healthcare](https://mlsanity.com/paper/2002.08596/) (2020)
- [Chester: A Web Delivered Locally Computed Chest X-Ray Disease Prediction System](https://mlsanity.com/paper/1901.11210/) (2019)

## Built on by (most cited first, 5 of 5)

- [SMILE: Single-turn to Multi-turn Inclusive Language Expansion via ChatGPT for Mental Health Support](https://mlsanity.com/paper/2305.00450/) (2023)
- [Achieving Trustworthy Real-Time Decision Support Systems with Low-Latency Interpretable AI Models](https://mlsanity.com/paper/2506.20018/) (2025)
- [FHIR-RAG-MEDS: Integrating HL7 FHIR with Retrieval-Augmented Large Language Models for Enhanced Medical Decision Support](https://mlsanity.com/paper/2509.07706/) (2025)
- [Development and Validation of a Deep-Learning Model for Differential Treatment Benefit Prediction for Adults with Major Depressive Disorder Deployed in the Artificial Intelligence in Depression Medication Enhancement (AIDME) Study](https://mlsanity.com/paper/2406.04993/) (2024)
- [PsyBridge: A Hybrid Intelligent Framework for Multi-Dimensional Mental Health Assessment and Decision Support](https://mlsanity.com/paper/2606.23673/) (2026)

## Abstract (arXiv)

Clinical decision support systems (CDSS) augmented with artificial intelligence (AI) models are emerging as potentially valuable tools in healthcare. Despite their promise, the development and implementation of these systems typically encounter several barriers, hindering the potential for widespread adoption. Here we present a case study of a recently developed AI-CDSS, Aifred Health, aimed at supporting the selection and management of treatment in major depressive disorder. We consider both the principles espoused during development and testing of this AI-CDSS, as well as the practical solutions developed to facilitate implementation. We also propose recommendations to consider throughout the building, validation, training, and implementation process of an AI-CDSS. These recommendations include: identifying the key problem, selecting the type of machine learning approach based on this problem, determining the type of data required, determining the format required for a CDSS to provide clinical utility, gathering physician and patient feedback, and validating the tool across multiple settings. Finally, we explore the potential benefits of widespread adoption of these systems, while balancing these against implementation challenges such as ensuring systems do not disrupt the clinical workflow, and designing systems in a manner that engenders trust on the part of end users.