Table 4.
Summary of key articles.
| Author | Key point |
|---|---|
| Johnsson et al.15 | A supervised ML model using ANN predicted neurological recovery and survival excellently, outperforming a conventional model based on logistic regression with prehospital setting carrying most details amongst data at time of hospitalization |
| Chou et al.16 | The generated ANN-boosted, CASPRI-based model exhibited good performance for predicting TTM neurological outcome, suggesting its clinical application to improve outcome prediction, facilitate decision-making, and formulate individualized therapeutic plans for patients receiving TTM |
| Chung et al.17 | The ANN models achieved highly accurate and reliable performance for predicting the neurological outcomes of successfully resuscitated patients with IHCA, which can assist with decision-making and optimal postresuscitation strategies |
| Jiang et al.18 | IBM Watson system includes both ML and NLP modules as is required of a successful AI system. Current regulations lack standards to assess the safety and efficacy of AI systems. AI systems need to be trained (continuously) by data from clinical studies. To provide incentives for sharing data on the system |
| Chung et al.19 | The ANN-based models achieved reliable performance to predict MNI and 3-month outcomes after thrombolysis for AIS to help assist in decision-making, especially when invasive adjuvant strategies are considered |
| Tjepkema-Cloostermans et al.20 | Deep learning of EEG signals outperforms any previously reported outcome predictor of coma after cardiac arrest, including visual EEG assessment by trained EEG experts |
| Viderman et al.21 | AI might be useful in predicting cardiac arrest, heart rhythm disorders, and postcardiac arrest outcomes, as well as in the delivery of drone-delivered defibrillators and notification of dispatchers |
| Zubler Tzovara22 | Existing studies show overall high performance in predicting outcome, relying either on spontaneous or on auditory evoked EEG signals |
| Andersson et al.23 | ANNs provided good to excellent prognostic accuracy in predicting neurological outcome in comatose patients post OHCA. The models which included NSE after 72 h and NFL on all days showed promising prognostic performance |
| Amacher et al.7 | Two postarrest scores (OHCA and CAHP) showed good prognostic accuracy for predicting neurological outcome after cardiac arrest and may support early discussions about goals-of-care and therapeutic planning on the ICU. A prearrest score (GO-FAR) showed acceptable prognostic accuracy and may support code status discussions |
| Mayampurath et al.8 | The gradient boosted machine algorithm was the most accurate for predicting favorable neurologic outcomes in IHCA survivors |
| Aqel et al.24 | The application of AI in prehospital emergency care has shown promise in detecting shockable rhythms, predicting resuscitation success, and enhancing CPR quality through real-time feedback. AI’s potential extends to predicting neurological outcomes after resuscitation and even addressing cardio-oncology cardiac arrests, improving risk prediction and resource allocation. Limitations - the need for large, annotated datasets, scarce quality-controlled rhythm annotations, regulatory challenges, and vulnerability to adversarial attacks. Future studies are needed to address data quality and biases, advance the interpretability of AI models, and ensure robust security measures |
| Amacher et al.25 | ChatGPT-4 showed a similar performance in predicting mortality and poor neurological outcome compared to validated postcardiac arrest scores. However, more research is needed regarding illogical answers for potential incorporation of an LLM in the multimodal outcome prognostication after cardiac arrest |
| Kawai et al.26 | A ML model using head CT images with transfer learning was used to predict the neurological outcomes at 1 month. It had superior accuracy to conventional methods and could help optimize treatment |
| Nagendran et al.27 | Most nonrandomised trials are not prospective, are at high risk of bias, and deviate from existing reporting standards. Data and code availability are lacking in most studies, and human comparator groups are often small. Future studies should diminish risk of bias, enhance real-world clinical relevance, improve reporting and transparency, and appropriately temper conclusions |
| Liyanage et al.28 | We need to ensure meticulous design and evaluation of AI applications. The primary care informatics community needs to be proactive and to guide the ethical and rigorous development of AI applications so that they will be safe and effective |
| Reddy et al.29 | Concerns include the possibility of biases, lack of transparency with certain AI algorithms, privacy concerns with the data used for training AI models, and safety and liability issues with AI application in clinical environments which need to be addressed |
| Daneshjou et al.30 | Three issues in datasets that are used to develop and test clinical AI algorithms for skin disease include sparsity of data set characterization and lack of transparency, nonstandard and unverified disease labels, and inability to fully assess patient diversity used for algorithm development and testing |
| Stanfill and Marc31 | HIM professionals are in a unique position to take on emerging roles with their depth of knowledge on the sources and origins of healthcare data. The challenge is to identify leading practices for the management of healthcare data and information in an AI-enabled world |
| Kagiyama et al.32 | The capability of AI to analyze unstructured data expands the field of cardiovascular research. In addition, AI will further increase its contribution to mobile health, computational modeling, and synthetic data generation, with new regularizations for its legal and ethical issues |
| Bahrami and Forouzanfar33 | The proposed deep learning approach was successful in forecasting the occurrence of sleep apnea from single-lead ECG. It can therefore be adopted in wearable sleep monitors for the management of sleep apnea |
| Hatami et al.34 | CNN-LSTM based ensemble mode offers an original way to automatically encode the spatio-temporal context of MR images in a deep learning architecture surpassing baseline |
| Wei et al.35 | Radiomics features extracted from DWI and ADC images can serve as valuable biomarkers for predicting poor clinical outcomes in patients with AIS. Furthermore, when these radiomics features were combined with multiclinical features, the predictive performance was enhanced. The prediction model has the potential to provide guidance for tailoring rehabilitation therapies based on individual patient risks for poor outcomes |
| Liu et al.36 | In China, most of the treatment recommendations of WFO are consistent with the recommendations of the expert group and can improve efficiency, although a relatively high proportion of cases are still not supported by WFO and it needs to learn regional characteristics to improve assistive ability. Therefore, WFO cannot currently replace oncologists |
ADC, apparent diffusion coefficient; AIS, acute ischemic stroke; ANN, artificial neural network; CAHP, cardiac arrest hospital prognosis; CASPRI, cardiac arrest survival postresuscitation in-hospital; CNN-LSTM, convolutional neural network, long short-term memory; DWI, diffusion-weighted imaging; EEG, electroencephalogram; GO-FAR, good outcome following attempted resuscitation; HIM, health information management professionals; IHCA, in-hospital cardiac arrest; ML, machine learning; MNI, major neurologic improvement; NFL, neurofilament light; NSE, neuron-specific enolase; OHCA, out-of-hospital cardiac arrest; TTM, targeted temperature management; WFO, Watson for oncology.