Evidence map›Paper›PMID 41624788›Full record

ArticleMayo Clinic proceedings. Digital health2026

Automotive Health 2.0: Steering Toward Proactive Preventive Care.

Dominik Naumann, Tatjana Amler, Doreen Schoeppenthau, Sergej Holzmann, Jörg Preißinger, Matthias Franz, Heyo K Kroemer, Alexander Meyer

Abstract read
In one paragraph

Article in Mayo Clinic proceedings. Digital health, 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

8 authors.

Dominik NaumannDeutsches Herzzentrum der Charité, Institute for Artificial Intelligence in Medicine, Berlin, Germany.
Tatjana AmlerCharité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Doreen SchoeppenthauDeutsches Herzzentrum der Charité, Institute for Artificial Intelligence in Medicine, Berlin, Germany.
Sergej HolzmannBMW Group Research and Technology, Munich, Germany.
Jörg PreißingerBMW Group Research and Technology, Munich, Germany.
Matthias FranzBMW Group Research and Technology, Munich, Germany.
Heyo K KroemerCharité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Alexander MeyerDeutsches Herzzentrum der Charité, Institute for Artificial Intelligence in Medicine, Berlin, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cardiovascular and chronic disease prevention remains limited by episodic, clinic-based assessments that fail to capture physiological changes arising in daily life. As mobility constitutes one of the most stable and repetitive environments people inhabit, vehicles offer a unique setting for subliminal, continuous health monitoring. This narrative presents the rationale and foundational framework for Automotive Health 2.0, a clinically oriented paradigm that transforms connected vehicles into validated platforms for physiological sensing, data integration, and proactive care delivery. Building on existing in-cabin cameras, radar, and microphones, multimodal algorithms enable unobtrusive estimation of cardiovascular, respiratory, and behavioral parameters during routine driving. Technological innovation lies in combining these signals with artificial intelligence-driven analytics to detect early disease signatures, support dynamic risk assessment, and enable adaptive telemonitoring directly linked to electronic health records. Clinically, this approach distinguishes regulatory-grade monitoring from consumer wellness tools by prioritizing accuracy, reproducibility, and integration with established workflows. Patients gain earlier detection and more equitable access to preventive care; clinicians receive continuous actionable data, and health systems benefit from scalable population-level monitoring. Automotive Health 2.0 positions the vehicle as a novel extension of the health care ecosystem, embedding validated prevention seamlessly into everyday life.

Identifiers

PMID41624788
PMCPMC12856176

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.