Evidence map›Paper›PMID 41516581›Full record

ReviewSensors (Basel, Switzerland)2025

AI-Driven Smart Cockpit: Monitoring of Sudden Illnesses, Health Risk Intervention, and Future Prospects.

Donghai Ye, Kehan Liu, Chenfei Luo, Ning Hu

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2025. 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

4 authors.

Donghai YeSchool of Arts, Chongqing University, Chongqing 400044, China.ORCID 0009-0006-4400-630X
Kehan LiuSchool of Arts, Chongqing University, Chongqing 400044, China.
Chenfei LuoSchool of Automation, Chongqing University, Chongqing 400044, China.
Ning HuSchool of Bioengineering, Chongqing University, Chongqing 400044, China.ORCID 0000-0002-6902-3302

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Intelligent driving cabins operated by artificial intelligence technology are evolving into the third living space. They aim to integrate perception, analysis, decision making, and intervention. By using multimodal biosignal acquisition technologies (flexible sensors and non-contact sensing), it is possible to monitor the physiological indicators of heart rate and blood pressure in real time. Leveraging the benefits of domain controllers in the vehicle and edge computing helps the AI platform reduce data latency and enhance real-time processing capabilities, as well as integrate the cabin's internal and external data through machine learning. Its aim is to build tailored health baselines and high-precision risk prediction models (e.g., CNN, LSTM). This system can initiate multi-level interventions such as adjustments to the environment, health recommendations, and ADAS-assisted emergency parking with telemedicine help. Current issues consist of sensor precision, AI model interpretation, security of data privacy, and whom to attribute legal liability to. Future development will mainly focus on cognitive digital twin construction, L4/L5 autonomous driving integration, new biomedical sensor applications, and smart city medical ecosystems.

Indexed as

Artificial IntelligenceDigital HealthHeart RateHumansIntelligent SystemsMachine LearningMonitoring, PhysiologicSoft ComputingTelemedicinedisease surveillanceintelligent vehicle cockpitrisk intervention

Identifiers

PMID41516581
PMCPMC12787685

What OpenQuestion holds

Textmetadata
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.