Revolutionizing healthcare: the role of artificial intelligence in clinical practice

BMC Medical Education, Journal Year: 2023, Volume and Issue: 23(1)

Published: Sept. 22, 2023

Abstract Introduction Healthcare systems are complex and challenging for all stakeholders, but artificial intelligence (AI) has transformed various fields, including healthcare, with the potential to improve patient care quality of life. Rapid AI advancements can revolutionize healthcare by integrating it into clinical practice. Reporting AI’s role in practice is crucial successful implementation equipping providers essential knowledge tools. Research Significance This review article provides a comprehensive up-to-date overview current state practice, its applications disease diagnosis, treatment recommendations, engagement. It also discusses associated challenges, covering ethical legal considerations need human expertise. By doing so, enhances understanding significance supports organizations effectively adopting technologies. Materials Methods The investigation analyzed use system relevant indexed literature, such as PubMed/Medline, Scopus, EMBASE, no time constraints limited articles published English. focused question explores impact applying settings outcomes this application. Results Integrating holds excellent improving selection, laboratory testing. tools leverage large datasets identify patterns surpass performance several aspects. offers increased accuracy, reduced costs, savings while minimizing errors. personalized medicine, optimize medication dosages, enhance population health management, establish guidelines, provide virtual assistants, support mental care, education, influence patient-physician trust. Conclusion be used diagnose diseases, develop plans, assist clinicians decision-making. Rather than simply automating tasks, about developing technologies that across settings. However, challenges related data privacy, bias, expertise must addressed responsible effective healthcare.

Language: Английский

---

Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond

Information Fusion, Journal Year: 2021, Volume and Issue: 77, P. 29 - 52

Published: July 31, 2021

Explainable Artificial Intelligence (XAI) is an emerging research topic of machine learning aimed at

Language: Английский

---

A systematic literature review of artificial intelligence in the healthcare sector: Benefits, challenges, methodologies, and functionalities

Journal of Innovation & Knowledge, Journal Year: 2023, Volume and Issue: 8(1), P. 100333 - 100333

Published: Jan. 1, 2023

Administrative and medical processes of the healthcare organizations are rapidly changing because use artificial intelligence (AI) systems. This change demonstrates critical impact AI at multiple activities, particularly in related to early detection diagnosis. Previous studies suggest that can raise quality services industry. AI-based technologies have reported improve human life quality, making easier, safer more productive. study presents a systematic review academic articles on application sector. The initially considered 1,988 from major scholarly databases. After careful review, list was filtered down 180 for full analysis present classification framework based four dimensions: AI-enabled benefits, challenges, methodologies, functionalities. It identified continues significantly outperform humans terms accuracy, efficiency timely execution administrative processes. Benefits patients' map directly relevant functionalities categories diagnosis, treatment, consultation health monitoring self-management chronic conditions. Implications future research directions areas value-added decision-making, security privacy patient data, features, creative IT service delivery models using AI.

Language: Английский

---

Patient and general public attitudes towards clinical artificial intelligence: a mixed methods systematic review

The Lancet Digital Health, Journal Year: 2021, Volume and Issue: 3(9), P. e599 - e611

Published: Aug. 23, 2021

Artificial intelligence (AI) promises to change health care, with some studies showing proof of concept a provider-level performance in various medical specialties. However, there are many barriers implementing AI, including patient acceptance and understanding AI. Patients' attitudes toward AI not well understood. We systematically reviewed the literature on general public clinical (either hypothetical or realised), quantitative, qualitative, mixed methods original research articles. searched biomedical computational databases from Jan 1, 2000, Sept 28, 2020, screened 2590 articles, 23 which met our inclusion criteria. Studies were heterogeneous regarding study population, design, field type under study. Six (26%) assessed currently available soon-to-be tools, whereas 17 (74%) broadly defined The quality these was mixed, frequent issue selection bias. Overall, patients conveyed positive but had reservations preferred human supervision. summarise findings six themes: concept, acceptability, relationship humans, development implementation, strengths benefits, weaknesses risks. suggest guidance for future studies, goal supporting safe, equitable, patient-centred implementation

Language: Английский

---

Machine Learning for Healthcare Wearable Devices: The Big Picture

Journal of Healthcare Engineering, Journal Year: 2022, Volume and Issue: 2022, P. 1 - 25

Published: April 18, 2022

Using artificial intelligence and machine learning techniques in healthcare applications has been actively researched over the last few years. It holds promising opportunities as it is used to track human activities vital signs using wearable devices assist diseases’ diagnosis, can play a great role elderly care patient’s health monitoring diagnostics. With technological advances medical sensors miniaturization of electronic chips recent five years, more are being developed for devices. Despite remarkable growth smart watches other devices, these massive research efforts have found their way market. In this study, review different areas presented. Different challenges facing on discussed. Potential solutions from literature presented, open improvement further highlighted.

Language: Английский

---

Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues

Cluster Computing, Journal Year: 2022, Volume and Issue: 26(4), P. 2271 - 2311

Published: Aug. 17, 2022

Language: Английский

---

Artificial Intelligence Trust, Risk and Security Management (AI TRiSM): Frameworks, applications, challenges and future research directions

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 240, P. 122442 - 122442

Published: Nov. 16, 2023

Language: Английский

---

Ethical, legal, and social considerations of AI-based medical decision-support tools: A scoping review

International Journal of Medical Informatics, Journal Year: 2022, Volume and Issue: 161, P. 104738 - 104738

Published: March 14, 2022

Language: Английский

---

Implementation Frameworks for Artificial Intelligence Translation Into Health Care Practice: Scoping Review

Journal of Medical Internet Research, Journal Year: 2021, Volume and Issue: 24(1), P. e32215 - e32215

Published: Dec. 27, 2021

Background Significant efforts have been made to develop artificial intelligence (AI) solutions for health care improvement. Despite the enthusiasm, professionals still struggle implement AI in their daily practice. Objective This paper aims identify implementation frameworks used understand application of Methods A scoping review was conducted using Cochrane, Evidence Based Medicine Reviews, Embase, MEDLINE, and PsycINFO databases publications that reported frameworks, models, theories concerning care. focused on studies published English investigating since 2000. total 2541 unique were retrieved from screened titles abstracts by 2 independent reviewers. Selected articles thematically analyzed against Nilsen taxonomy Greenhalgh framework nonadoption, abandonment, scale-up, spread, sustainability (NASSS) technologies. Results In total, 7 met all eligibility criteria inclusion review, included formal directly addressed implementation, whereas other provided limited descriptions elements influencing implementation. Collectively, identified aligned with NASSS domains, but no single article comprehensively considered factors known influence technology New domains identified, including dependency data input existing processes, shared decision-making, role human oversight, ethics population impact inequality, suggesting do not fully consider needs Conclusions literature demonstrates understanding how practice is its early stages development. Our findings suggest further research needed provide knowledge necessary guide future clinical highlight opportunity draw field science.

Language: Английский

---

Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries

Molecular Diversity, Journal Year: 2021, Volume and Issue: 26(3), P. 1893 - 1913

Published: Oct. 23, 2021

Language: Английский

---