Neural engineering: the process, applications, and its role in the future of medicine
Bibliographic record
Abstract
OBJECTIVE: Recent advances in neural engineering have restored mobility to people with paralysis, relieved symptoms of movement disorders, reduced chronic pain, restored the sense of hearing, and provided sensory perception to individuals with sensory deficits. APPROACH: This progress was enabled by the team-based, interdisciplinary approaches used by neural engineers. Neural engineers have advanced clinical frontiers by leveraging tools and discoveries in quantitative and biological sciences and through collaborations between engineering, science, and medicine. The movement toward bioelectronic medicines, where neuromodulation aims to supplement or replace pharmaceuticals to treat chronic medical conditions such as high blood pressure, diabetes and psychiatric disorders is a prime example of a new frontier made possible by neural engineering. Although one of the major goals in neural engineering is to develop technology for clinical applications, this technology may also offer unique opportunities to gain insight into how biological systems operate. MAIN RESULTS: Despite significant technological progress, a number of ethical and strategic questions remain unexplored. Addressing these questions will accelerate technology development to address unmet needs. The future of these devices extends far beyond treatment of neurological impairments, including potential human augmentation applications. Our task, as neural engineers, is to push technology forward at the intersection of disciplines, while responsibly considering the readiness to transition this technology outside of the laboratory to consumer products. SIGNIFICANCE: This article aims to highlight the current state of the neural engineering field, its links with other engineering and science disciplines, and the challenges and opportunities ahead. The goal of this article is to foster new ideas for innovative applications in neurotechnology.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".