Artificial Intelligence in Healthcare: A Scoping Review of Perceived Threats to Patient Rights and Safety DOI Creative Commons
Nkosi Nkosi Botha, Edward Wilson Ansah, Cynthia Esinam Segbedzi

et al.

Research Square (Research Square), Journal Year: 2023, Volume and Issue: unknown

Published: Oct. 23, 2023

Abstract Health systems worldwide are facing unprecedented pressure as the needs and expectations of patients increase get ever more complicated. The global health system is thus,forced to leverage on every opportunity, including artificial intelligence (AI), provide care that consistent with patients’ needs. Meanwhile, there serious concerns about how AI tools could threaten rights safety. Therefore, this study maps available evidence,between January 1, 2010 September 30, 2023, perceived threats posed by usage in healthcare We deployed guidelines based Tricco et al. conduct a comprehensive search literature from Nature, PubMed, Scopus, ScienceDirect, Dimensions, Ebsco Host, ProQuest, JStore, Semantic Scholar, Taylor & Francis, Emeralds, World Organisation, Google Scholar. In keeping inclusion exclusions thresholds, 14 peer reviewed articles were included study. report potential for breach privacy, prejudice race, culture, gender, social status, also subject errors commission omission. Additionally, existing regulations appeared inadequate define standards use healthcare. Our findings have some critical implications achieving Sustainable Development Goals (SDGs) 3.8, 11.7, 16. recommend national governments should lead rollout healthcare, key actors industry contribute developing policies countries invest sponsor research into their system.

Language: Английский

Improving the local diagnostic explanations of diabetes mellitus with the ensemble of label noise filters DOI
Che Xu, Peng Zhu, Jiacun Wang

et al.

Information Fusion, Journal Year: 2025, Volume and Issue: unknown, P. 102928 - 102928

Published: Jan. 1, 2025

Language: Английский

Citations

4
A deep neural network with modified random forest incremental interpretation approach for diagnosing diabetes in smart healthcare DOI
Toly Chen, Hsin‐Chieh Wu, Min-Chi Chiu

et al.

Applied Soft Computing, Journal Year: 2023, Volume and Issue: 152, P. 111183 - 111183

Published: Dec. 22, 2023

Language: Английский

Citations

29
Use of Artificial Intelligence in Triage in Hospital Emergency Departments: A Scoping Review DOI Open Access

Samantha Tyler,

Matthew Olis,

Nicole Aust

et al.

Cureus, Journal Year: 2024, Volume and Issue: unknown

Published: May 8, 2024

The integration of artificial intelligence (AI) and machine learning (ML) in healthcare has become a major point interest raises the question its impact on emergency department (ED) triaging process. AI's capacity to emulate human cognitive processes coupled with advancements computing shown positive outcomes various aspects but little is known about use AI patients ED. algorithms may allow for earlier diagnosis intervention; however, overconfident answers present dangers patients. purpose this review was explore comprehensively recently published literature regarding effect ML ED triage identify research gaps. A systemized search conducted September 2023 using electronic databases EMBASE, Ovid MEDLINE, Web Science. To meet inclusion criteria, articles had be peer-reviewed, written English, based primary data studies US journals 2013-2023. Other criteria included 1) needing admitted hospital EDs, 2) must have been used when patient, 3) patient represented. controlled descriptors from Medical Subject Headings (MeSH) that terms "artificial intelligence" OR "machine learning" AND "emergency ward" care" department" room" "patient triage" "triage" "triaging." initially identified 1,142 citations. After rigorous, screening process critical appraisal evidence, 29 were selected final review. findings indicated models consistently demonstrated superior discrimination abilities compared conventional systems, into yielded significant enhancements predictive accuracy, disease identification, risk assessment, accurately determined necessity hospitalization requiring urgent attention, 4) improved resource allocation quality care, including predicting length stay. suggested superiority prioritizing holds potential redefine precision.

Language: Английский

Citations

12
Explainable Artificial Intelligence (XAI) in healthcare: Interpretable Models for Clinical Decision Support DOI

Nitin Rane,

Saurabh Choudhary,

Jayesh Rane

et al.

SSRN Electronic Journal, Journal Year: 2023, Volume and Issue: unknown

Published: Jan. 1, 2023

In healthcare, the incorporation of Artificial Intelligence (AI) plays a pivotal role in enhancing diagnostic precision and guiding treatment decisions. Nevertheless, lack transparency conventional AI models poses challenges gaining trust clinicians comprehending rationale behind their This research paper explores Explainable (XAI) its application with specific focus on transparent designed for clinical decision support various medical disciplines. The initiates by underscoring crucial requirement interpretability systems within healthcare realm. Recognizing diverse nature specialties, study investigates tailored XAI approaches to meet distinctive needs areas such as radiology, pathology, cardiology, oncology. Through thorough review existing literature analysis, identifies key obstacles prospects implementing across varied contexts. field cornerstone imaging, proves beneficial elucidating decision-making procedures image analysis algorithms. probes into impact interpretable radiological diagnoses, examining how can seamlessly integrate AI-generated insights workflows. Within where is utmost importance, clarifies enhance histopathological assessments. By demystifying intricacies AI-driven pathology models, aims empower pathologists leverage these tools more accurate diagnoses. Cardiology, characterized complex interplay physiological parameters, benefits from offering intelligible explanations cardiovascular risk predictions recommendations. delves highlighting potential systems. Moreover, oncology, decisions hinge precise identification characterization tumors, aids unraveling intricate machine learning models. This, turn, fosters among oncologists utilizing personalized strategies.

Language: Английский

Citations

13
Ambient Intelligence (AmI) DOI
Toly Chen

SpringerBriefs in applied sciences and technology, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 21

Published: Jan. 1, 2024

Language: Английский

Citations

4
XAmI Applications to Location-Aware Services DOI
Toly Chen

SpringerBriefs in applied sciences and technology, Journal Year: 2024, Volume and Issue: unknown, P. 63 - 83

Published: Jan. 1, 2024

Language: Английский

Citations

4
Unraveling the Black Box: A Review of Explainable Deep Learning Healthcare Techniques DOI Creative Commons
Nafeesa Yousuf Murad, Mohd Hilmi Hasan, Muhammad Hamza Azam

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 66556 - 66568

Published: Jan. 1, 2024

Language: Английский

Citations

4
Rebuilding Semiconductor Manufacturing Competitiveness and Sustainability Through Supply Chain Localization DOI
Toly Chen

Published: Jan. 1, 2025

Language: Английский

Citations

0
Localization of Semiconductor Supply Chains—Driving Forces and Challenges DOI
Toly Chen

Published: Jan. 1, 2025

Language: Английский

Citations

0
An effective PO-RSNN and FZCIS based diabetes prediction and stroke analysis in the metaverse environment DOI Creative Commons

M. Karpagam,

S. Sarumathi,

Anuj Maheshwari

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 4, 2025

Abstract Chronic disease (CD) like diabetes and stroke impacts global healthcare extensively, continuous monitoring early detection are necessary for effective management. The Metaverse Environment (ME) has gained attention in the digital environment; yet, it lacks adequate support disabled individuals, including deaf dumb people, also faces challenges security, generalizability, feature selection. To overcome these limitations, a novel probabilistic-centric optimized recurrent sechelliott neural network (PO-RSNN)-based prediction (DP) Fuzzy Z-log-clipping inference system (FZCIS)-based severity level estimation ME is carried out. proposed integrates Montwisted-Jaco curve cryptography (MJCC) secured data transmission, Aransign-principal component analysis (A-PCA) dimensionality reduction, synthetic minority oversampling technique (SMOTE) to address imbalance. diagnosed results securely stored BlockChain (BC) enhanced privacy traceability. experimental validation demonstrated superior performance of by achieving 98.97% accuracy DP 98.89% analysis, outperforming existing classifiers. Also, MJCC attained 98.92% efficiency, surpassing traditional encryption models. Thus, produces secure, scalable, highly accurate ME. Further, research will extend approach other CD cancer heart improve predictive performance.

Language: Английский

Citations

0