Digital Innovations for Clinical Assessment in Acquired Brain Injury: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Acquired brain injury (ABI) is a leading global cause of morbidity, affecting millions, many of whom face a diverse range of cognitive, physical, and psychological challenges, often made worse due to limited access to timely assessment and appropriate care. In recent years, digital technologies have emerged as potential tools to support more accessible, efficient, and scalable methods for assessment; however, the breadth of research in this area remains unclear. OBJECTIVE: This scoping review aimed to identify and synthesize contemporary research on how digital technologies may help screen, assess, and monitor the complications of ABI in order to uncover trends, themes, and priorities for future research. METHODS: Following the Arksey and O'Malley framework and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, a systematic search was conducted across Embase, MEDLINE, and Scopus, as well as four clinical trial registries to help capture gray literature. A search string incorporating terms related to "ABI," "clinical assessment," and "digital tools" was developed a priori. Studies from 2013 to 2024 leveraging digital health tools, for example, smartphones, tablets, websites, telemedicine, and virtual reality, to aid ABI complication assessment were included. Exclusion criteria comprised studies involving bespoke clinical hardware (eg, radiographs and EEG monitors), nonhuman subjects, or review articles. Following this, data synthesis and domain mapping were performed. RESULTS: Of 5293 screened records, 88 met the inclusion criteria: 2 retrospective studies, 4 qualitative studies, 35 cohort studies, 42 cross-sectional studies, and 5 randomized controlled trials. The median sample included 26 participants with ABI; 51 studies also involved non-ABI participants (median of 10 participants included). Digital platforms varied, with 45 studies using smartphone or tablet technologies, 23 PC or web-based platforms, 11 telemedicine solutions, and 9 virtual reality platforms. The predominant research themes included the use of digital technology to aid screening for traumatic brain injury, identifying or monitoring symptoms or functional outcomes; physical examination, the assessment of cognition and communication, and providing a comprehensive consultation. Most tools were reported to be well-tolerated, with accuracy often described as comparable to standard assessments. However, the studies were heterogeneous, with limited validation of tools across broad representative populations or multiple sites or studies. There was also little discussion on potential ethical concerns such as accessibility, access, and data privacy. CONCLUSIONS: This investigation provides an extensive overview of current research trends and highlights the need for larger, more rigorous studies to optimize the use of digital technologies in ABI assessment, as well as gaps in the assessment of common complications. Expanding research into underexplored ABI complications, broadening the scope of assessments to include a broader range of complications, and including larger, more diverse populations will be critical for advancing the field and improving outcomes for individuals with ABI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.129 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.032 | 0.029 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".