Evidence map›Paper›PMID 42509357›Full record

ReviewCommunications medicine2026

Advancing data protections for implantable brain-computer interfaces.

Julian D Sandbrink, Michael J Young

Abstract readReview
In one paragraph

Review in Communications medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Julian D SandbrinkHarvard Medical School, Boston, MA, USA.ORCID http://orcid.org/0000-0001-5452-8845
Michael J YoungHarvard Medical School, Boston, MA, USA. michael.young@mgh.harvard.edu.ORCID http://orcid.org/0000-0001-6661-0811

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Implantable brain-computer interfaces (iBCIs) are rapidly transitioning from proof-of-concept devices to early clinical application. The high-resolution neural signals they capture may yield insights beyond those derived from conventional health data. In this Review, we examine how clinical iBCI data remain insufficiently protected, despite existing privacy laws like the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR). Five core gaps are identified: overreliance on conventional de-identification, limited individual control and rights, conflated consent practices, limited guardrails against misuse, and underspecified ownership. We examine strategies to address these gaps, including protections for de-identified data, stronger iBCI data rights and control, separate data consent, limits on harmful secondary uses, and monetization guardrails. As iBCIs transition from research tools to real-world clinical practice, clinicians, researchers, developers, and regulators, in dialogue with prospective and current iBCI users, will play central roles in advancing patient autonomy and privacy.

Identifiers

PMID42509357
PMCPMC13408982

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.