Unlocking Solutions: AI For Disaster Management and Psychosocial Well-being

As disruptive technologies such as artificial intelligence emerge, new potential for enhancing disaster resilience and risk reduction are being addressed. AI-driven models are increasingly proving successful for earthquake and flood monitoring. One example is the M-LARGE project, where scientists combined satellite images with deep learning models in order to provide an early warning signal before strong shaking and tsunami onsets. This innovation consistently outperformed conventional methods, improving predictions in previously affected areas. Another example is the use of neural networks in urban flood nowcasting, referring to the near-future prediction of flood status in the wake of extreme weather unfolding. Here, researchers used community features, such as tweets, 311 calls etc. to model /predict the unfolding of a flooding event. This helps the community to anticipate risks and impacts and can create situational awareness.

Along with the set of merits AI is offering, Kuglitsch et al., address the limitations of this new technology, stating that it remains crucial in realising its benefits.

Like any tool, the effectiveness of an AI model depends on the information it's built upon. Therefore, data quantity is the first challenge. In disaster situations, the focus often leans towards the 'response and recovery' phase. However, a crucial challenge lies in gathering sufficient enough data on different aspects. Even though the frequency of disasters has increased over the last few years, their occurrence is still relatively rare. This limits the amount of data available for training AI-based models, thus restricting their development and generalizability. Second, there is the problem of data quality. Here, technical factors like calibration, spatiotemporal resolution, and independency come into play. A third problem has little to do with the actual models but their implementation. When it comes to using AI to tackle disasters, an important piece of the puzzle needs attention. You see, AI algorithms are usually crafted by experts in universities or labs. They create these tools that can make a real difference. But here's the catch – once they're done, the results often end up in conferences, research papers, or committee meetings. They might not reach the people who can actually put them to use in the real world – like governments and decision-makers. That's a challenge we need to overcome.

We reiterate that the critical strength of AI lies in its ability to analyse vast amounts of data quickly and identify patterns from large data sets and settings. In mental health, AI can analyse data from multiple sources, such as electronic health records, social media platforms, and wearable devices, to detect trends, patterns, and correlations and help identify at-risk individuals. We summarise the five key points (Figure 2) and insights that hold the potential to provide actors, agencies and institutions with the appropriate knowledge to better assist vulnerable individuals after a traumatic event and to make optimal use of scarce resources.  

First, despite the current limitations due to the scarcity of research in this area, AI’s (machine learning methods) potential for future investigations to capitalise on advancements and leverage indicators derived from social media and electronic health records remains promising. Current AI applications in mental health mainly focus on mental disorders like schizophrenia and other psychotic disorders. This indicates a significant gap in our understanding of how they can be used to study other mental health conditions.

Second, in the context of mental health, enhanced cross-sectoral collaboration is vital to ensure the successful integration of new technologies for the benefit of public health programs and policies. The intersection holds significant promise for advancing mental health diagnosis, treatment, and support.

Third, addressing challenges like ethical considerations and data privacy should also be considered when implementing AI in mental health scenarios. Safeguards must be in place to protect individuals' privacy and ensure responsible use of data. Establishing transparent and accountable practices for data collection, storage, and sharing is crucial to building trust and maintaining the confidentiality and security of sensitive information.

Fourth, identifying pathways to leverage the critical strengths of AI that lie in its ability to analyse vast amounts of data quickly and identify patterns that might be challenging (or time-consuming) for humans to discern. In mental health, AI can analyse data from multiple sources, such as electronic health records, social media platforms, and wearable devices, to detect trends, patterns, and correlations and help identify at-risk individuals. This enables timely interventions and personalised support services, alleviating the burden on healthcare professionals.

Fifth, while AI offers exciting possibilities in supporting mental health and psychosocial well-being, we must remember that it should complement, not replace, human care. The human touch, empathy, and therapeutic alliance remain irreplaceable in mental healthcare. AI should be seen as a tool to augment and enhance mental health services, empowering clinicians and individuals with valuable insights and resources. 

It is fair to say that AI holds the potential to support disaster risk reduction and related mental healthcare. By leveraging its unique characteristics, we envision a future where climate services are better integrated, more effective, and tailored to individual or community needs and sustainability goals (see Annexure 1). Yet, as we venture into this crossroads of AI and mental health, let's tread carefully. As AI innovations transform into tangible solutions, the vision should strike a balance between technological advancements and human connections, ensuring that the well-being and dignity of individuals and communities remain at the core of understanding, designing, and applying solutions.