Article Review: "Artificial intelligence and machine learning in prehospital emergency care: A scoping review"
The article titled "Artificial intelligence and machine learning in prehospital emergency care: A scoping review" by Marcel Lucas Chee, Mark Leonard Chee, Haotian Huang, Katelyn Mazzochi, Kieran Taylor, Han Wang, Mengling Feng, Andrew Fu Wah Ho, Fahad Javaid Siddiqui, Marcus Eng Hock Ong, and Nan Liu, provides a comprehensive overview of the current landscape of artificial intelligence (AI) applications in prehospital emergency care (PEC). It explores various aspects of AI implementation in PEC settings, ranging from predictive models to resource optimization and continuous monitoring systems.
The authors commence with a well-structured introduction that aptly highlights the growing interest in AI's potential in PEC. The mention of diverse applications of AI in PEC sets the stage for readers to grasp the breadth of this evolving field. The focus on evidence map analysis is commendable, as it helps identify existing gaps in AI implementation, emphasizing the need for further research.
The article effectively highlights the most prevalent application of AI in PEC, which is triage and prognostication. This is a crucial area where AI can make a significant impact, given the time-sensitive nature of prehospital care. The examples provided, such as Liu’s work on ML prognostic models, illustrate the potential of AI in assessing risk and facilitating timely interventions.
Additionally, the discussion of AI's role in out-of-hospital cardiac arrest (OHCA) is insightful. AI algorithms predicting defibrillation success and patient outcomes after OHCA demonstrate how AI can influence early intervention and treatment decisions for high-risk patients. This application showcases the life-saving potential of AI in PEC.
The article's exploration of AI's contribution to optimization problems within PEC, such as response time improvement and ambulance demand prediction, is well-articulated. The use of AI-assisted dispatch systems and genetic algorithms for drone positioning highlights AI's capacity to enhance the efficiency of EMS operations and ultimately save lives.
The identification of emerging use cases, including patient trial matching using clinical notes and the integration of wearable Internet of Things (IoT) devices, adds depth to the review. These emerging applications, particularly wearable IoT devices, hold promise in providing real-time data for remote continuous monitoring. However, the article aptly notes that these systems are in their infancy and require further validation.
The article appropriately acknowledges the importance of rigorous validation and reporting in AI studies. The caution regarding the potentially optimistic performance metrics is well-founded, and the call for improved validation and reporting aligns with best practices in AI research. The mention of AI-specific guidelines such as SPIRIT-AI and CONSORT-AI is a valuable addition, as it promotes transparency and reproducibility in AI studies.
One of the strengths of the article lies in its discussion of AI's advantages, particularly its ability to handle high-dimensional data and integrate multimodal inputs. The inclusion of examples, such as the use of nonlinear modeling and natural language processing (NLP) for multimodal EHR data analysis, underscores AI's versatility and potential for innovation in PEC.
The article also recognizes the interpretability challenge posed by AI models, often perceived as "black boxes." The discussion on explainable AI as a potential solution is timely, as it highlights the importance of understanding AI decision-making processes in clinical contexts. The authors rightly emphasize the need for AI applications to be both performant and interpretable.
In conclusion, "Artificial intelligence and machine learning in prehospital emergency care: A scoping review" offers a well-structured and insightful overview of AI's role in PEC. It effectively communicates the potential benefits and challenges of AI implementation while emphasizing the need for validation, transparency, and responsible AI usage in this critical healthcare domain.
https://www.sciencedirect.com/science/article/pii/S2589004223014840
