CoChat
Sign Up
Cited by 481Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AIInternet of Things and AI

The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.

Drawbacks of artificial intelligence and their potential solutions in the healthcare sector.

Open access
khan, Bangul, Fatima, Hajira, Qureshi, Ayatullah, Kumar, Sanjay, Hanan, Abdul, Hussain, Jawad, Abdullah, Saad·2023·Biomedical Materials & Devices
DOI 10.1007/s44174-023-00063-2PMC PMC9908503
Open this paper in CoChat — free accountOpen PDF

Abstract

Artificial intelligence (AI) has the potential to make substantial progress toward the goal of making healthcare more personalized, predictive, preventative, and interactive. We believe AI will continue its present path and ultimately become a mature and effective tool for the healthcare sector. Besides this AI-based systems raise concerns regarding data security and privacy. Because health records are important and vulnerable, hackers often target them during data breaches. The absence of standard guidelines for the moral use of AI and ML in healthcare has only served to worsen the situation. There is debate about how far artificial intelligence (AI) may be utilized ethically in healthcare settings since there are no universal guidelines for its use. Therefore, maintaining the confidentiality of medical records is crucial. This study enlightens the possible drawbacks of AI in the implementation of healthcare sector and their solutions to overcome these situations.Graphical abstract

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1007/s44174-023-00063-2

Cites

52 of 52 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 51 references

Research works printed but not linked: 5

  1. J. Habermann, Psychological impacts of COVID-19 and preventive strategies: A review. (2021).
  2. A. Holzinger, C. Biemann, C. S. Pattichis, D. B. Kell, What do we need to build explainable AI systems for the medical domain? (2017). https://arXiv.Org/:1712.09923.
  3. B. Lubarsky, Re-identification of “anonymized data.” UCLA L. Rev. 1701, 1754 (2010)
  4. H. Wang, D.-Y. Yeung, Towards Bayesian deep learning: a framework and some existing methods. IEEE Trans. Knowl. Data Eng. 28, 3395–3408 (2016) · DOI
  5. D. Miley, L.B. Machado, C. Condo, A.E. Jergens, K.-J. Yoon, S. Pandey, Video capsule endoscopy and ingestible electronics: emerging trends in sensors, circuits, materials, telemetry, optics, and rapid reading software. Adv. Devices Instrum. 2021, 1–30 (2021). https://doi.org/10.3433/2021/9854040 · DOI

Cited by

Sorted by citations

Items 301–400 of 481

Related

See all

Metadata

Authors
khan, B., Fatima, H., Qureshi, A., Kumar, S., Hanan, A., Hussain, J., & Abdullah, S.
Affiliations
  • Riphah International University (PK)
  • Mehran University of Engineering and Technology (PK)
  • NED University of Engineering and Technology (PK)
Corresponding author
khan, Bangul
ORCID
Venue
Biomedical Materials & Devices
Publisher
Springer Science and Business Media LLC
Published
2023-02-08
Volume
1
Issue
2
Pages
731-738
ISSN
  • 2731-4812 (ISSN-L)
  • 2731-4820
DOI
10.1007/s44174-023-00063-2
PMC
PMC9908503
OpenAlex
W4319454440
Cited by
461
References
51
Citations by year
2026: 64 · 2025: 166 · 2024: 185 · 2023: 46
FWCI
5.42
Type
journal article
Language
en
Open access
Licence
Locations
  • Biomedical Materials & Devices (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • University of Engineering and Technology, Lahore
  • Mälardalens högskola
  • Mehran University of Engineering and Technology
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
Keywords
artificial intelligence in healthcare, data security, privacy, health records, data breaches, ethical use of AI, machine learning ethics, medical records confidentiality

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review