AI and Neurotrauma: Where are we now?

WHERE AI IS ALREADY CHANGING THE LANDSCAPE

AI has demonstrated substantial benefits across neuro care, from enabling early diagnosis to supporting patient-specific recovery. In a recent review by Toy et al.² a growing number of AI models using pre-hospital data (vital signs, mechanism of injury, dispatcher narratives, etc.) were shown to aid early decisions in a more objective and data-driven triage, which could ensure severely injured patients are identified and transported to appropriate trauma centers. Most of these models employed ML (88%) alone or with DL or NLP and were used to predict the need for critical care interventions in 29% of the studies, 22% focused on triage assistance, and 20% on survival prediction. These AI systems can automatically analyze incoming data, extracting features from ambulance call transcripts, calculating trauma scores, or continuously monitoring vital sign trends, thereby providing real-time risk stratification to Emergency medical services (EMS) clinicians. Although this area is still emerging, the evidence suggests AI can complement field triage protocols and potentially improve outcomes by expediting definitive care.

A relevant area where AI has rapidly impacted is neuroimaging analysis and diagnosis. Accurate detection of hemorrhages, hematomas, contusions, and axonal injuries on computed tomography (CT) and magnetic resonance imaging (MRI) is vital for timely treatment. However, subtle lesions in mild TBI are often overlooked by humans due to their small size and diffuse pattern. AI algorithms excel at conventional, precise approaches to image recognition within seconds.³ Food and Drug Administration-approved ML tools are available and supported for automated brain CT interpretation. For instance, Icometrix’s Icobrain TBI can automatically quantify intracranial lesions with accuracy comparable to expert radiologists. Its MRI counterpart identifies diffuse axonal injury lesions and even correlates lesion burden with functional results, thereby strengthening diagnostic capability.⁴

Furthermore, combining imaging findings with clinical parameters, such as the Glasgow Coma Scale and vital signs, enables ML models to stratify TBI severity and identify patients at higher risk of deterioration. In spinal cord injury, AI can improve diagnostic precision, with convolutional neural networks demonstrating the capacity to automatically segment cord lesions and determine both injury level and completeness with accuracy surpassing that of conventional assessments.⁵

AI also holds promise for targeted treatments and interventions, with algorithms capable of optimizing surgical strategies by integrating imaging and anatomical data. In spine surgery, Rasouli et al.⁶ demonstrated that AI can refine patient selection criteria and predict outcomes with DL support, ultimately contributing to safer and more effective procedures. Similarly, in neurocritical care, the main priority is to develop tailored injury profiles and assessments.

Once initial steps have been completed, neurotrauma care requires decisions aimed at anticipating intracranial hypertension (ICH) or cerebral edema. In a systematic review of algorithms for predicting ICH, Babikir et al.⁷ reported that ML algorithms using multimodal intracranial data can predict ICH episodes up to half an hour before clinical onset, enabling proactive intervention and potentially preventing secondary injury. Similarly, AI prognostication supports clinicians in treatment planning and in counseling patients or families with greater confidence. Malhotra et al.⁸ further demonstrated that ensemble models outperformed traditional prognostic scores, including International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) and Corticoid Randomization after Significant Head Injury (CRASH), in predicting mortality and functional outcomes in moderate-to-severe Traumatic Brain Injury (TBI).⁸

Beyond the acute phase, AI is increasingly contributing to rehabilitation and recovery in both brain and spinal injuries, with a major advantage of providing personalized, real-time feedback and adjusting therapy to the patient’s progress. Kim et al.⁵ examined the role of AI-powered exoskeletons, brain–computer interfaces, and advanced rehabilitation planning systems in SCI recovery. Their work emphasized the promising capacity of these technologies to deliver personalized interventions by incorporating motion tracking during physiotherapy sessions, while also enabling automatic task adjustment and corrective feedback to enhance motor relearning. In addition, brain–computer interfaces combined with AI algorithms have opened new frontiers for patients with severe SCI, by interpreting neural signals and offering a form of neuroprosthetic rehabilitation. Thus, AI is transforming neurorehabilitation by providing adaptive therapy and extending care beyond hospital walls.

LIMITATIONS: WHERE THE PROMISE MEETS THE WALL

On a global scale, there has been a significant increase in AI-related neurotrauma publications, signaling a robust international research effort. However, much of this work is still dominated by high-income countries and adult datasets, leaving notable gaps. Furthermore, many AI models are developed using single-center data or non-representative populations, reducing their external validity.^1-10 Lee et al. cautioned about the risk of overfitting, emphasizing that models trained on limited datasets may perform well during training but often fail to generalize effectively in real clinical settings.¹¹ This idea is particularly relevant in neurotrauma, where injury mechanisms vary widely across patients, imaging protocols differ between hospitals, and regional care practices influence outcomes. Such heterogeneity highlights data bias as a major limitation, particularly when datasets are scarce, algorithms can produce inequitable or inaccurate outputs, a critical concern in urgent scenarios where even milliseconds may determine outcomes, and biased technologies can mean the difference between recovery and disability.¹²

Another critical limitation is the interpretability and reliability of these AI models. Many of them operate as a “black box,” thus not providing clear justifications for their outputs, which limits trust among clinicians.¹¹ Furthermore, system errors are a concern, such as alarming for injury when none exists, which can have serious consequences in care.

Finally, a commonly cited limitation is that AI lacks human qualities integral to medicine, raising ethical and legal questions about data privacy, informed consent, liability for errors, and algorithmic bias. Regulatory bodies are necessary to validate patient rights. Similarly, AI cannot replicate the empathy or compassion involved in challenging conversations that support rehabilitation and human teamwork. These facts reflect the importance of active vigilance from clinicians, training professionals with knowledge in AI tools while emphasizing that AI is not a complete replacement for daily practices.

FUTURE DIRECTIONS AND EMERGING OPPORTUNITIES

In the coming years, the application of AI in neurotrauma is anticipated to broaden significantly. Addressing current limitations will require prioritizing the integration of multimodal datasets with advanced AI platforms capable of processing diverse information sources using adaptive, continuously learning algorithms, alongside rigorous external validation entities.¹³ Furthermore, AI’s integration into neurotrauma care demands robust, multicenter, international, prospective trials, supported by transparent and interpretable modeling approaches.³

One emerging innovation is the concept of “digital twins,” which involves generating a detailed virtual representation of a patient by leveraging large-scale AI-driven data integration. Such models can be used to simulate disease evolution and test therapeutic strategies. In neurotrauma, this scenario of a TBI patient could allow clinicians to see how that patient might respond to different intracranial pressure management strategies or to predict complications before they occur. Kim et al.⁵ emphasized digital twin technology as a potentially transformative approach for the future of neurotrauma care, owing to its revolutionary advantages. Realizing such sophisticated models will require surmounting data-sharing barriers and ensuring computational infrastructure in hospitals.

Interdisciplinary collaboration among clinicians, engineers, ethicists, and federal regulators is essential to foster ethical, equitable, and safe adoption by healthcare professionals. AI must be framed not as a replacement for human expertise, but as an augmentation tool that increases precision, equity, and the safety of clinical decisions. Global progress in this area requires investment in collaborative infrastructures that facilitate secure data exchange and cross-border model development, while prioritizing inclusive, multilingual, and flexible AI systems capable of addressing diverse patient needs.⁹

CONCLUSION

AI holds the potential to revolutionize neurotrauma care through different approaches and personalized decision-making. Real progress lies not in perfect models, but in responsible design, ethical deployment, and equitable access.

The discipline now confronts a crucial challenge: Will AI applications in neurotrauma amplify existing inequities or instead become a catalyst for more equitable and evidence-based care worldwide? The answer depends not on machines, but on us.