No two brain or spinal cord injuries are ever the same. Each recovery journey is shaped by different bodies, different stories, and different forms of resilience. Artificial intelligence (AI) allows therapy to adapt in real time, analyzing a patient’s movement, strength, and progress, then tailoring exercises and interventions to meet them exactly where they are. It’s not about replacing the therapist’s touch. It’s about using human expertise with intelligent precision, giving every patient a more personalized path to regain strength, independence, and confidence.
Can AI Really Help With Therapy? New Research Says Yes—With Limits
Artificial intelligence is increasingly showing up in mental health spaces, but can a chatbot actually help people feel better? A new study highlighted in Psychology Today suggests that, under the right conditions, AI-driven therapy tools may offer real benefits.
Researchers at Dartmouth College tested an AI chatbot called Therabot, designed specifically to deliver cognitive behavioral therapy (CBT), a well-established, evidence-based approach for conditions like depression and anxiety. Unlike general-purpose chatbots, Therabot was trained on structured CBT techniques and monitored by clinical experts to ensure safety and appropriateness.
In a randomized clinical trial involving adults with depression, generalized anxiety disorder, and eating disorders, participants who used Therabot experienced meaningful symptom reductions. Depression symptoms dropped by more than half on average, anxiety symptoms decreased substantially, and eating-related concerns also improved. These outcomes were comparable to improvements typically seen in traditional CBT.
One of the most surprising findings was the strength of the therapeutic alliance. Participants reported levels of trust and engagement with the AI that were like those formed with human therapists—an important factor, as therapeutic alliance is a strong predictor of treatment success. Many users engaged with the chatbot during moments of distress, especially outside normal therapy hours, highlighting AI’s potential to increase access to support.
Researchers are careful to emphasize that AI therapy tools are not replacements for human clinicians. Instead, they may serve as a supplement—particularly for people facing barriers such as cost, long waitlists, or limited availability of providers. Complex emotional work, crisis intervention, and individualized care still require human judgment and connection.
Artificial Intelligence and Traumatic Brain Injury: Promise, Progress, and Caution
Artificial intelligence (AI) is increasingly being explored as a tool to improve how traumatic brain injury (TBI) is diagnosed and how outcomes are predicted. A 2025 scoping review published in the International Journal of Emergency Medicine examined the current state of clinical research on AI—including machine learning (ML) and deep learning (DL) in TBI care.
Several studies showed AI can enhance interpretation of brain imaging, such as CT, MRI, and functional MRI. Machine learning models were able to detect subtle abnormalities and classify injury patterns with greater precision than manual review alone. In one pediatric study, a deep learning model distinguished children with TBI from those without injury with nearly 83% accuracy—suggesting AI could help identify injuries that might otherwise be missed.
AI was also used to predict outcomes such as mortality, hospital length of stay, and functional recovery. Some models achieved high predictive accuracy, with performance comparable to or better than traditional prognostic tools. Importantly, models that combined imaging data with clinical information outperformed approaches relying on standard clinical measures alone.
The review highlights several potential advantages of AI in TBI care; one of the most exciting is the integration of complex data, allowing clinicians to consider imaging, clinical scores, and other variables together.
Despite promising results, the authors caution against over-interpretation; the small number of studies, limited sample sizes, and lack of standardized datasets raise concerns about generalizability. AI may become a useful supportive tool in TBI diagnosis and prognosis, but further research and validation are needed before it can be relied upon in routine clinical or medicolegal decision-making.
Artificial Intelligence and Spinal Cord Injury: What the Evidence Shows
Artificial intelligence (AI) is gaining attention as a potential tool to improve diagnosis and outcome prediction in spinal cord injury (SCI). A 2025 systematic review published in Brain and Spine examined how AI techniques—including machine learning and deep learning—are being used in clinical SCI research and how well these tools perform.
AI has been applied primarily to imaging and clinical data to improve injury classification and severity assessment. Several studies showed that AI models can analyze MRI and other imaging data to identify injury characteristics and distinguish between injury types with greater consistency than manual interpretation alone. This suggests AI may help detect subtle features of spinal cord damage that are difficult to identify through conventional methods.
It was also used to predict outcomes such as neurological recovery, functional independence, and complications following SCI. Models that combined imaging findings with clinical variables—such as injury level, severity, and early neurological exam results—generally performed better than traditional prediction approaches. In some cases, AI-based models demonstrated predictive accuracy comparable to established clinical scoring systems.
Despite promising findings, the review has some problems. Many studies relied on small datasets, single-center data, and retrospective designs, raising concerns about generalizability. Differences in imaging protocols, outcome measures, and model transparency also limit real-world application. Importantly, many AI models function as “black boxes,” making it difficult to understand how predictions are generated—an issue with clear implications for clinical and legal settings.
AI shows promise in supporting diagnosis and prognosis in spinal cord injury, particularly by integrating complex imaging and clinical data. However, the evidence base remains limited. Larger, standardized, and externally validated studies are needed before AI tools can be reliably incorporated into routine clinical care or relied upon in high-stakes decision-making.
Artificial Intelligence at Trial: What It Means in Brain and Spinal Cord Injury Cases
Artificial intelligence (AI) is increasingly used in personal injury cases involving traumatic brain injury (TBI) and spinal cord injury (SCI). It is used as a support tool to help attorneys and experts manage complex medical and legal information. One of the most common applications is medical record review. AI can quickly sift through large volumes of records, flag inconsistencies, identify timelines from injury to treatment, and highlight patterns in imaging, lab results, or clinical notes. While AI speeds up the process, humans still interpret the data and testify about their findings in court.
AI also assists in imaging analysis for conditions like traumatic brain injury, spinal cord injury, or orthopedic trauma. Advanced software can detect subtle changes in CT or MRI scans, classify injury patterns, and estimate severity. It is also used for prognosis and outcome modeling, because it can help estimate the likelihood of recovery, predict functional outcomes, and model future care needs using clinical and demographic data.
These tools may inform expert opinions or support life care planning, but courts treat AI outputs as advisory, not determinative. Similarly, AI may assist economists in projecting medical costs, lost earning capacity, or other damages, but all final opinions remain the responsibility of human experts.
AI does not diagnose injuries on its own, decide whether someone is disabled, determine fault or damages, or replace medical judgment. When used in a trial, the court will focus on whether the information is reliable, whether its limits are understood, and whether a qualified expert can explain and defend the conclusions.
Brain and spinal cord injuries are complex and often don’t show up clearly on a single scan. Symptoms can change over time and affect many aspects of daily life, and AI can help clinicians analyze patterns across time in large to help doctors manage information more efficiently.
AI does not decide liability, replace medical experts, testify in court, determine causation, or override clinical judgment. It is a powerful analytical tool—but not an authority. It supports expert analysis and helps streamline complex information, while human expertise and judgment remain central to medical and legal decision-making.
The expertise and the personal touch of the lawyers of Cantor Grana Buckner Bucci isn’t because of AI; it’s because they care, they understand injuries and the life-long effects they can have. They feel personally responsible for helping their clients regain their quality of life, and fight hard to make it so.
