Artificial intelligence (AI) is the branch of computer science dealing with the simulation
of intelligent behavior in computers.[1] Computers play a key role in almost every aspect of our daily life. In healthcare,
computers are an excellent means of storage of patient-related data. The amount of
data gleaned electronically from patients admitted in the intensive care units (ICUs)
has been growing rapidly every day. Several equipment, such as pressure transducers,
infusion pumps, electrocardiography (ECG), pulse oximeters, cardiac output monitors,
fluids intake and output monitors, temperature, neurological examination, and mechanical
ventilators, interface with computers and store electronic data. Similarly, a wealth
of information is recorded from each patient in the ICU, including high-resolution
physiological signals, various laboratory tests, and details of medical history in
electronic health records (EHRs).[2] Computerized ICU systems interface, in turn, provide access to hospital database,
including demographic, electronic patient records, order entry, laboratory, pharmacy,
and radiological systems. To be of use, it is necessary that ICU bedside data must
be extracted and organized to become information for clinical decisions.[3] AI can assist not only in administering repetitive patient assessment in real time,
but also in integrating and interpreting these data source with EHR data, thus potentially
enabling more timely and targeted interventions.[4]
[5] Closed-loop AI systems can monitor parameters of patients; then, directly treat
patients and induce changes in those very parameters that are undergoing monitoring.
These systems can make direct real-time adjustments to patient care without any human
input.[6] AI has proved effective in lowering cost, expanding access, and improving healthcare
fields. The application of AI in medicine has been related to the development of AI
programs, intended to help the clinician in the making of a diagnosis, adopting therapeutic
decisions, and forecasting outcomes. It plays a pivotal role by forewarning impending
complications, thereby resulting in a faster response by the clinician.[7] AI in an ICU setting could decrease clinicians’ as well as nurses’ workload, thereby
allowing them to focus their attention on critical tasks. It could also augment human
decision-making by offering low-cost, high-capacity intelligent data processing.
Utility in Neurocritical Care Units
Utility in Neurocritical Care Units
Deployment of AI in neurocritical care units (NCCUs) is gradually becoming a reality
with the availability of technology. AI systems have made tremendous progress in the
realm of analysis of high-resolution neurocritical care data as well as algorithm
decision-making.[8] AI systems will have a significant impact on NCCUs as they are equipped with an
array of technologically sophisticated implements to capture and store patient parameters
in detail.
NCCUs involve the management of complex neurological patients with inherent limitations
of clinical assessment because of the injured brain. Multimodality monitoring generates
voluminous data in NCCUs, which can be analyzed with the help of AI.[8] Thus, introducing AI in NCCUs will immensely benefit the healthcare providers and
patients alike. It is highly likely that traumatic brain injured patients may get
the most benefits from AI. It can predict elevation in intracranial pressure (ICP)
by advance ICP pulse analysis so that a proactive ICP management could be realized
based on these accurate forecasts.[9] Self-organizing fuzzy logic control (FLC) can administer propofol to provide more
stable sedation to forestall the effects of agitation on ICP in traumatic brain injured
patients on mechanical ventilation. FLC can compensate for interpatient variation
of propofol need.[10] Furthermore, another algorithm has the ability to predict future mean ICP, which
can enable clinicians to identify dangerous trends in ICP early.[11] Similarly, hypotension, which too adversely impacts outcome of TBI patients, can
be predicted beforehand by a Bayesian artificial neural network model. Thus, an early warning of potential hypotensive event before it emerges would allow
close monitoring and early clinical assessment to prevent onset of hypotension.[12] AI would revolutionize management of mechanical ventilation, which is a frequent
intervention in neurologically injured patients in NCCU, by ensuring more personalized
sedation and analgesia, avoiding unnecessary hyperventilation, and patient readiness
for extubation.[13]
Automated detection of seizures from various seizure detecting monitors, prediction
of medication response, and even the adjustment of antiepileptic drugs will reduce
morbidity in epilepsy patients.[14] AI is going to play a greater role in the early management of ischemic stroke and
prediction of sepsis in these patients. These patients are generally elderly who may
develop delirium in the course of NCCU stay. Overall incidence of delirium is very
high in NCCU.[15] Delirious patients have more complications and may have even worse rehabilitation.
AI-enabled data analysis could improve detection of delirium and enable real-time
intervention to improve sleep hygiene.[16]
Limitations and Risks
Of course, using technology to transfer education and healthcare carries some risks.
Safeguarding the privacy of patient records must be a top priority because healthcare
data are sensitive; so, data security is an important consideration. Appropriate consent
must be obtained for data collection; yet many critically ill patients lack sufficient
capacity until recovery.
AI may save time, but it cannot listen to a patient. Physical examination will remain
important for diagnosis. No one should think that that AI-enabled diagnostic tools
will replace doctors or that online learning platforms would supplant teachers, especially
when it comes to developing the socioemotional skills.
While AI may enable the designing and development of accurate tools, their introduction
must follow careful considerations of real-time clinical utility. The use of AI should
be appropriately weighted alongside other sources of available information and should
be validated by well-designed prospective studies. Organizations such as Food and
Drug Administration (FDA), the Clinical Decision Support Coalition, and Harvard University
are offering guidelines on how to move forward with AI in a safe, ethical, and sustainable
manner that supports better care while avoiding doomsday scenario if some algorithm
goes haywire. Moreover, storing large database in a single location makes the repository
a very attractive target for hackers. The full impact of AI in NCCU cannot be discerned
yet as applications still remain in their infancy.
