AI and machine learning in resuscitation: Ongoing research, new concepts, and key challenges

Abstract

Aim: Artificial intelligence (AI) and machine learning (ML) are important areas of computer science that have recently attracted attention for their application to medicine. However, as techniques continue to advance and become more complex, it is increasingly challenging for clinicians to stay abreast of the latest research. This overview aims to translate research concepts and potential concerns to healthcare professionals interested in applying AI and ML to resuscitation research but who are not experts in the field.

Main text: We present various research including prediction models using structured and unstructured data, exploring treatment heterogeneity, reinforcement learning, language processing, and large-scale language models. These studies potentially offer valuable insights for optimizing treatment strategies and clinical workflows. However, implementing AI and ML in clinical settings presents its own set of challenges. The availability of high-quality and reliable data is crucial for developing accurate ML models. A rigorous validation process and the integration of ML into clinical practice is essential for practical implementation. We furthermore highlight the potential risks associated with self-fulfilling prophecies and feedback loops, emphasizing the importance of transparency, interpretability, and trustworthiness in AI and ML models. These issues need to be addressed in order to establish reliable and trustworthy AI and ML models.

Conclusion: In this article, we overview concepts and examples of AI and ML research in the resuscitation field. Moving forward, appropriate understanding of ML and collaboration with relevant experts will be essential for researchers and clinicians to overcome the challenges and harness the full potential of AI and ML in resuscitation.

Keywords: Emergency medicine; Feedback loop; Heterogeneity; Large language model; Natural language processing; Prediction model; Self-fulfilling prophecy.

Fig. 1

The concept of prediction models applied to predict mortality A prediction model is one type of ML developed to predict the outcome. Various patterns of clinical information can be utilized to develop prediction models.

Fig. 2

The concept of clustering and sub-phenotypes Phenotypes (e.g., sepsis, acute respiratory distress syndrome) are categorized by clustering to sub-phenotypes with different clinical features and the heterogeneous response to the treatment.

Fig. 3

The concept of treatment heterogeneity (Left) Assuming that the difference between outcomes when treatment is performed and when it is not, is the same in each patient: treatment effect is homogenous between individual patients. (Right) Assuming that the difference between outcomes when treatment is performed and when it is not, is different in each patient: treatment effect is heterogenous between individual patients.

Fig. 4

The concept of reinforcement learning in medical research. Patient status is changed to a different status by the action, and consequently, the reward is obtained based on the status. Reinforcement learning can find the best strategy to maximize the rewards based on many trials.

Fig. 5

Example of Natural Language Processing for Activating Bystander CPR NLP: Natural Language Processing, CPR: Cardiopulmonary resuscitation In the emergency call dispatch center, the application utilizes natural language processing (NLP) to analyze the caller's words, aiding the dispatcher in identifying potential cases of cardiac arrest.

Fig. 6

Concept of self-fulfilling prophecy and its feedback loop. A particular patient who could be saved is assessed as “Very low possibility to survive” by the inaccurate and biased prediction, which can potentially lead to the decision-making of treatment withdrawal. As a result, the initial prediction “Very low possibility to survive” is realized. If this data is utilized to develop the ML models, it can amplify and reproduce the false prediction, which lead to the potential harm that the patients lose the opportunity to be treated by the enhanced inaccurate prediction.