Artificial Intelligence (AI) has the potential to revolutionize healthcare by improving patient outcomes, reducing costs, and enhancing clinical decision-making. In particular, AI-driven approaches can be valuable in monitoring patients in intensive care units (ICUs), where timely and accurate decision-making can be critical for patient survival.
A recent systematic review conducted by a team of researchers from Germany and the United States investigated the use of AI and machine learning (ML) models for clinical decision support (CDS) in monitoring cardiovascular patients in ICUs. The study reviewed 89 papers published between January 2018 and August 2022, and identified 21 studies that met the criteria for the final qualitative assessment.
The review found that clinical time series and electronic health records (EHR) data were the most common input modalities for AI/ML models, while gradient boosting, recurrent neural networks (RNNs), and reinforcement learning (RL) were the most frequently used methods for analysis. However, the review also identified several challenges that need to be addressed for effective integration of AI in healthcare.
One key challenge is the generalizability issue. 75% of the selected papers lacked validation against external datasets, meaning that the AI models were not tested on independent data sets, which could impact their ability to generalize to new patients or healthcare settings.
Another significant challenge is interpretability. While AI models can be highly accurate in predicting patient outcomes, it can be challenging to interpret their decisions and understand how they arrive at their conclusions. This lack of transparency can limit the trust that clinicians and patients have in these models, hindering their adoption in clinical practice.
Despite these challenges, the study highlights the potential of AI and ML models in clinical decision-making and the need for continued research and development to overcome these obstacles. AI-assisted clinical decision support has the potential to improve patient outcomes and reduce healthcare costs, but it must be used judiciously and responsibly, with appropriate validation and interpretability measures in place.
In conclusion, the study provides important insights into the current state of AI-driven methodologies in intensive patient monitoring and highlights the challenges and opportunities in integrating AI into clinical practice. As AI technology continues to evolve, it will be essential to address these challenges and work collaboratively with healthcare professionals to ensure that AI-assisted clinical decision support improves patient outcomes and healthcare delivery.