Deployment of Artificial Intelligence in Neuro Critical Care
Readers of Telehealth and Medicine Today might ask, “What is the relevance of artificial intelligence (AI) to telehealth and telemedicine?” Not surprisingly, the answer lies, at least in part, in the application of AI through telehealth to mediate the issues of misdistribution of the demand versus supply of healthcare services. That and more are probably possible. Understanding the current status of AI in healthcare is the first step and the goal of this editorial.
In the last few years, we have witnessed unprecedented growth and development in the deployment of AI in healthcare. Simultaneously, super-specialization and sub-specialization niches in clinical areas have also increased. Neurocritical care (NCC) is one such area. This communication discusses the present status of some of the clinical challenges within NCC that AI could address. This includes unlocking clinically relevant information hidden in massive amounts of data. In an NCC setup, there are limitations and even inefficiencies in traditional approaches. It is possible that, eventually, AI could help mitigate some of these issues. However, at present, we are in a stage of transition. We still do not have enough data to unequivocally identify all the use cases for AI in an NCC unit. The purist may require double-blind, randomized controlled, multi-institutional cross-over studies before advocating the use of AI in an NCC. However, this is a time-consuming, labor-intensive procedure with several challenges.
The “A” in artificial intelligence should ideally stand for Augmenting, Amplifying, Accelerating, and Assisting in an Ambient milieu. Today, AI is being adopted for the management of critically ill patients in an NCC unit. Immersed in voluminous dynamic data, secondary to multimodality monitoring (MMM), an intensivist could benefit from predictive analytics. Components of AI used in clinical practice include machine learning (ML), deep learning (DL), natural language processing (NLP), fuzzy logic (FL), convolutional neural networks (CNN), and data mining (DM). Big data (BD) are large, complex data sets that cannot be analyzed using traditional statistical modeling. These components of AI are increasingly being used in NCC. Converting computing power to meaningful clinical information is a challenge. The use of AI needs a thorough, systematic evaluation before incorporation into management.
NCC is primarily real-time, dynamic management of critically ill patients with neurological disorders with multi-factorial compromised brain functions. Management includes real-time analysis of large volumes of scores from different types of data. MMM includes close monitoring of ventilation parameters, intracranial pressure (ICP), hemodynamics, body temperature, fluid intake-output, and serial neurological examinations. In addition to electrophysiological monitoring of brain and cardiac functions, AI predicts earlier neurological deterioration, enabling better management and outcomes. Predicting a rise in ICP, sub-clinical seizures, and maintaining pulmonary functions are AI-enabled illustrations.
Want to read more? Head here: https://doi.org/10.30953/thmt.v9.502
Krishnan Ganapathy, MCh (Neurosurgery), FACS, FICS, FAMS, PhD | Director, Apollo Telemedicine Networking Foundation & Apollo Tele Health Services, Chennai, Tamilnadu, India
