Privacy-preserving artificial intelligence in healthcare: Techniques and applications

Khalid, Nazish; Qayyum, Adnan; Al‐Fuqaha, Ala; Qadir, Junaid

doi:10.1016/j.compbiomed.2023.106848

Cited by 98 publications

(95 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As ML applications in healthcare continue to expand, it is crucial to consider the regulatory and ethical implications of their use [73, 74]. Ensuring patient privacy, securing data, and maintaining transparency in AI decision-making processes are paramount [75, 76]. Future research must also address these aspects to foster a safe and trustful adoption of AI technologies in healthcare.…”

Section: Discussionmentioning

confidence: 99%

Recent Advancements in Machine Learning-Based Bloodstream Infection Prediction: A Systematic Review and Meta-analysis of Diagnostic Test Accuracy

Bopche,

Damås

2024

Preprint

View full text Add to dashboard Cite

Abstract. Purpose: Bloodstream infections (BSIs) present significant public health challenges. With the advent of machine learning (ML), promising predictive models have been developed. This study evaluates their performance through a systematic review and meta-analysis. Methods: We performed a comprehensive systematic review across multiple databases, including PubMed, IEEE Xplore, ScienceDirect, ACM Digital Library, SpringerLink, Web of Science, Scopus, and Google Scholar. Eligible studies focused on BSIs within any hospital setting, employing ML models as the diagnostic test. We evaluated the risk of bias with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklist and assessed the quality of evidence using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach. Models reporting the area under the receiver operating characteristic curve (AUROC) were included in the meta-analysis to identify key performance drivers. Results: After screening, a total of 30 studies were eligible for synthesis, from which 41 models and 8 data types were extracted. Most of the studies were carried out in the inpatient settings (n=17; 56%), followed by the emergency department (ED) settings (n=7; 23%), and followed by the ICU settings (n=6; 20%). The reported AUROCs in the hospital inpatients settings, ranged from 0.51-0.866, in the ICU settings AUROCs ranged from 0.668-0.970, and in the emergency department (ED) settings the AUROCs of the models ranged from 0.728-0.844. One study reported prospective cohort study, while two prospectively validated their models. In the meta-analysis, laboratory tests, Complete Blood Count/Differential Count (CBC/DC), and ML model type contributed the most to model performance. Conclusion: This systematic review and meta-analysis show that on retrospective data, individual ML models can accurately predict BSIs at different stages of patient trajectory. Although they enable early prediction of BSI, a comprehensive approach to integrate data types and models is necessary. Systematic reporting, externally validated, and clinical implementation studies are needed to establish clinical confidence.

show abstract

Section: Discussionmentioning

confidence: 99%

Recent Advancements in Machine Learning-Based Bloodstream Infection Prediction: A Systematic Review and Meta-analysis of Diagnostic Test Accuracy

Bopche,

Damås

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Arti cial intelligence and its utilisation in governance, academia, manufacturing, security, entertainment, space and marine exploration, health, etcetera, is gaining popularity among researchers globally [20,25]. There are several studies about the utility of AI tools in other elds [10], yet very few studies in patient-care [13]. Moreover, most of the studies that examined the application of AI tools in health were not conducted in the area of patient-care [2].…”

Section: Discussionmentioning

confidence: 99%

“…When effectively combined with human reasoning, AI tools have the ability to accurately establish patterns, subtle and complex correlations in large and high-dimensional datasets that often escape the traditional techniques [8,11]. Though their full adoption into healthcare is yet to be realised, there is evidence of wide application of AI tools in patient-care globally [10,12,13]. So far, AI applications in patient-care are getting more sophisticated, effective, and e cient in supporting clinical and administrative decisions [2,12].…”

Section: Introductionmentioning

confidence: 99%

“…Despite the growing research about AI application in other elds worldwide, there seems to be inadequate evidence about AI utilisation in the eld of healthcare [13,25]. Moreover, most of the studies involving AI use in health were conducted in non-clinical circles and devoid of the views of patients and other key stakeholders in healthcare [2].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligent Tools: Evidence-Mapping on the Perceived Positive Effects on Patient-Care

Botha,

Ansah,

Segbedzi

et al. 2023

Preprint

View full text Add to dashboard Cite

The global healthcare system is fraught with serious challenges including scarcity of critical healthcare professionals, changes in disease patterns, pandemics, access and equity issues among others. Considering that there is no quick fix to the myriad of healthcare challenges, World Health Organisation proposed a full integration of artificial intelligent (AI) tools into patient-care to stimulate efficiency and guarantee quality in patient-management. Therefore, this review maps evidence on the perceived positive effects of AI tools on patient-care. The review considered time expand between January 1, 2010 and October 31, 2023. Consistent with the protocol by Tricco et al., a comprehensive literature search was executed fromNature, PubMed, Scopus, ScienceDirect, Dimensions, Ebsco Host, ProQuest, JStore, Semantic Scholar, Taylor & Francis, Emeralds, WHO, and Google Scholar. Upholding the inclusion and exclusion standards, 14 peer reviewed articles were included in this review. We report the use of that AI tools could significantly improve accuracy of clinical diagnosis and guarantee better health-outcomes of patients. AI tools also have the ability to mitigate, if not eliminate, most of the factors that currently predict poor patient outcomes. Furthermore, AI tools are far more efficient in generating robust and accurate data in real-time, and could help ease and accelerate the workflow. If properly integrated into the healthcare system, AI could help accelerate the attainment of Sustainable Development Goals 3.4, 3.8, and 3.b. We propose that AI developers collaborate with public health practitioners and healthcare managers to develop AI applications that appreciate socio-cultural dimensions in patient-care.

show abstract

“…AI, while beneficial in various aspects of care, cannot replace the essential human elements of empathy and compassion in mental health nursing (Woodnutt et al, 2024). The safeguarding of data privacy and security also remains a critical concern, necessitating stringent protective measures for sensitive patient information (Khalid et al, 2023).…”

Section: A I I N T H E M E N Ta L H E a Lt H N U R Si Ngmentioning

confidence: 99%

Critical review of self‐diagnosis of mental health conditions using artificial intelligence

Wimbarti,

Kairupan,

Tallei

2024

Int J Mental Health Nurs

View full text Add to dashboard Cite

The advent of artificial intelligence (AI) has revolutionised various aspects of our lives, including mental health nursing. AI‐driven tools and applications have provided a convenient and accessible means for individuals to assess their mental well‐being within the confines of their homes. Nonetheless, the widespread trend of self‐diagnosing mental health conditions through AI poses considerable risks. This review article examines the perils associated with relying on AI for self‐diagnosis in mental health, highlighting the constraints and possible adverse outcomes that can arise from such practices. It delves into the ethical, psychological, and social implications, underscoring the vital role of mental health professionals, including psychologists, psychiatrists, and nursing specialists, in providing professional assistance and guidance. This article aims to highlight the importance of seeking professional assistance and guidance in addressing mental health concerns, especially in the era of AI‐driven self‐diagnosis.

show abstract

Privacy-preserving artificial intelligence in healthcare: Techniques and applications

Cited by 98 publications

References 66 publications

Recent Advancements in Machine Learning-Based Bloodstream Infection Prediction: A Systematic Review and Meta-analysis of Diagnostic Test Accuracy

Recent Advancements in Machine Learning-Based Bloodstream Infection Prediction: A Systematic Review and Meta-analysis of Diagnostic Test Accuracy

Artificial Intelligent Tools: Evidence-Mapping on the Perceived Positive Effects on Patient-Care

Critical review of self‐diagnosis of mental health conditions using artificial intelligence

Contact Info

Product

Resources

About