Table 2.

Summary.

Objective	Role of AI in predicting neurological outcomes in postcardiac resuscitation
Methodology	Detailed narrative review of all recent studies of AI, ML algorithms, prediction tools, and assess their benefit regarding our objective as compared to traditional methods
Traditional methods	Clinical assessment like neurological examination (pupillary response, corneal reflex, and motor response), scoring systems (CAHP, OHCA, and CPC), blood biomarkers (NSE), electrophysiological methods (EEG), and radiological imaging (CT scan/MRI)
Use of AI	Advanced ML techniques and use of ANN improve accuracy of prognostic models when integrated with clinical data, enhance data interpretation, identify relevant predictive markers in diagnostics and design more efficient diagnostic/predictive methods. ML algorithms to analyze EEG and deep learning models to analyze MRI images improve accuracy of possible outcome and improve early diagnosis respectively as noted successfully on some studies. Helps resource optimization by early recognition of hypoxic brain injury. AI can help personalize healthcare and possibly help families make informed decisions based on reliable prognostic information
Limitations	Bias in population groups with different ethnic and socioeconomic backgrounds, lack of transparency and explainability, healthcare data security, accuracy and reliability
Future directions/goals	Digital imaging is becoming more vital. Improvement in fMRI and DTI enable noninvasive methods of brain visualization. Use of ChatGPT-4 functioned well in death and severe neurological prognosis. XGR had the best prediction accuracy compared to SVM and LR. CT scan models which can detect lung cancer with accuracy comparable or better than six radiologists. Ethical concerns and limitations should be addressed. Close human monitoring, ensuring proper data security, uses diverse population groups to reduce bias, have AI models explain clearly its process and rationale to improve transparency should all be integral goals to aim for moving forwards
Conclusion	Despite limitations, many advancements have been made by AI and its potential in our objective which appears promising. As the system continues to improve, it does need close human supervision, education, sharing collected data and aim to continually improve whilst manage limitations
Abbreviations	AI, artificial intelligence; ANN, artificial neural networks; CAHP, cardiac arrest hospital prognosis; CPC, cerebral performance categories; CT, computed tomography; DTI, diffusion tensor imaging; EEG, electroencephalogram; fMRI, functional MRI; LR, logistic regression; ML, machine learning; OHCA, out-of-hospital cardiac arrest; NSE, neuron specific enolase; SVM, support vector machine; XGR, extreme gradient boosting