Evidence map›Paper›PMID 36969684›Full record

ArticleFrontiers in public health2023

Toward explainable AI-empowered cognitive health assessment.

Abdul Rehman Javed, Habib Ullah Khan, Mohammad Kamel Bader Alomari, Muhammad Usman Sarwar, Muhammad Asim, Ahmad S Almadhor, Muhammad Zahid Khan

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Article in Frontiers in public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

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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

7 authors.

Abdul Rehman JavedDepartment of Cyber Security, Air University, Islamabad, Pakistan.
Habib Ullah KhanDepartment of Accounting and Information Systems, College of Business and Economics, Qatar University, Doha, Qatar.
Mohammad Kamel Bader AlomariDepartment of Accounting and Information Systems, College of Business and Economics, Qatar University, Doha, Qatar.
Muhammad Usman SarwarDepartment of Computer Games Development, Air University, Islamabad, Pakistan.
Muhammad AsimDepartment of Cyber Security, National University of Computer and Emerging Science, Islamabad, Pakistan.
Ahmad S AlmadhorCollege of Computer and Information Sciences, Jouf University, Sakakah, Saudi Arabia.
Muhammad Zahid KhanDepartment of Computer Science & IT, University of Malakand, Chakdara, Pakistan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Explainable artificial intelligence (XAI) is of paramount importance to various domains, including healthcare, fitness, skill assessment, and personal assistants, to understand and explain the decision-making process of the artificial intelligence (AI) model. Smart homes embedded with smart devices and sensors enabled many context-aware applications to recognize physical activities. This study presents

Indexed as

Artificial IntelligenceDementiaAwarenessBayes TheoremHumansNeural Networks, Computeradvanced sensorsassistive technologyexplainable AIhealthcarehuman activity recognitionInternet of Thingskey feature extraction

Identifiers

PMID36969684
PMCPMC10033697

What OpenQuestion holds

Textfull text, public
LicenceCC BY
measurements read69
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.