Towards evidence-based practice 2.0: leveraging artificial intelligence in healthcare DOI Creative Commons
Per Nilsén,

David Sundemo,

Fredrik Heintz

et al.

Frontiers in Health Services, Journal Year: 2024, Volume and Issue: 4

Published: June 11, 2024

Background Evidence-based practice (EBP) involves making clinical decisions based on three sources of information: evidence, experience and patient preferences. Despite popularization EBP, research has shown that there are many barriers to achieving the goals EBP model. The use artificial intelligence (AI) in healthcare been proposed as a means improve decision-making. aim this paper was pinpoint key challenges pertaining pillars investigate potential AI surmounting these contributing more evidence-based practice. We conducted selective review literature integration achieve this. Challenges with components Clinical decision-making line model presents several challenges. availability existence robust evidence sometimes pose limitations due slow generation dissemination processes, well scarcity high-quality evidence. Direct application is not always viable because studies often involve groups distinct from those encountered routine healthcare. Clinicians need rely their interpret relevance contextualize it within unique needs patients. Moreover, might be influenced by cognitive implicit biases. Achieving involvement shared between clinicians patients remains challenging factors such low levels health literacy among reluctance actively participate, rooted clinicians' attitudes, scepticism towards knowledge ineffective communication strategies, busy environments limited resources. assistance for promising solution address inherent process, conducting studies, generating synthesizing findings, disseminating crucial information implementing findings into systems have advantage over human processing specific types data information. great promise areas image analysis. avenues enhance engagement saving time increase autonomy although lack issue. Conclusion This underscores AI's augment practices, potentially marking emergence 2.0. However, also uncertainties regarding how will contribute Hence, empirical essential validate substantiate various aspects

Language: Английский

Artificial intelligence in the field of pharmacy practice: A literature review DOI Creative Commons
Sri Harsha Chalasani, Jehath Syed, Madhan Ramesh

et al.

Exploratory Research in Clinical and Social Pharmacy, Journal Year: 2023, Volume and Issue: 12, P. 100346 - 100346

Published: Oct. 21, 2023

Artificial intelligence (AI) is a transformative technology used in various industrial sectors including healthcare. In pharmacy practice, AI has the potential to significantly improve medication management and patient care. This review explores applications field of practice. The incorporation technologies provides pharmacists with tools systems that help them make accurate evidence-based clinical decisions. By using algorithms Machine Learning, can analyze large volume data, medical records, laboratory results, profiles, aiding identifying drug-drug interactions, assessing safety efficacy medicines, making informed recommendations tailored individual requirements. Various models have been developed predict detect adverse drug events, assist decision support medication-related decisions, automate dispensing processes community pharmacies, optimize dosages, adherence through smart technologies, prevent errors, provide therapy services, telemedicine initiatives. incorporating into health care professionals augment their decision-making patients personalized allows for greater collaboration between different healthcare services provided single patient. For patients, may be useful tool providing guidance on how when take medication, education, promoting know where obtain most cost-effective best communicate professionals, monitoring wearables devices, everyday lifestyle guidance, integrate diet exercise.

Language: Английский

Citations

66
Ethical considerations on artificial intelligence in dentistry: A framework and checklist DOI Open Access
Rata Rokhshad, Maxime Ducret, Akhilanand Chaurasia

et al.

Journal of Dentistry, Journal Year: 2023, Volume and Issue: 135, P. 104593 - 104593

Published: June 22, 2023

Language: Английский

Citations

53
Unmasking bias in artificial intelligence: a systematic review of bias detection and mitigation strategies in electronic health record-based models DOI
Feng Chen, Liqin Wang, Julie Hong

et al.

Journal of the American Medical Informatics Association, Journal Year: 2024, Volume and Issue: 31(5), P. 1172 - 1183

Published: March 23, 2024

Abstract Objectives Leveraging artificial intelligence (AI) in conjunction with electronic health records (EHRs) holds transformative potential to improve healthcare. However, addressing bias AI, which risks worsening healthcare disparities, cannot be overlooked. This study reviews methods handle various biases AI models developed using EHR data. Materials and Methods We conducted a systematic review following the Preferred Reporting Items for Systematic Reviews Meta-analyses guidelines, analyzing articles from PubMed, Web of Science, IEEE published between January 01, 2010 December 17, 2023. The identified key biases, outlined strategies detecting mitigating throughout model development, analyzed metrics assessment. Results Of 450 retrieved, 20 met our criteria, revealing 6 major types: algorithmic, confounding, implicit, measurement, selection, temporal. were primarily predictive tasks, yet none have been deployed real-world settings. Five studies concentrated on detection implicit algorithmic employing fairness like statistical parity, equal opportunity, equity. Fifteen proposed especially targeting selection biases. These strategies, evaluated through both performance metrics, predominantly involved data collection preprocessing techniques resampling reweighting. Discussion highlights evolving mitigate EHR-based models, emphasizing urgent need standardized detailed reporting methodologies testing evaluation. Such measures are essential gauging models’ practical impact fostering ethical that ensures equity

Language: Английский

Citations

37
Understanding and Mitigating Bias in Imaging Artificial Intelligence DOI
Ali S. Tejani, Yee S. Ng, Yin Xi

et al.

Radiographics, Journal Year: 2024, Volume and Issue: 44(5)

Published: April 18, 2024

Artificial intelligence (AI) algorithms are prone to bias at multiple stages of model development, with potential for exacerbating health disparities. However, in imaging AI is a complex topic that encompasses coexisting definitions.

Language: Английский

Citations

26
Ethical considerations and concerns in the implementation of AI in pharmacy practice: a cross-sectional study DOI Creative Commons
Hisham E. Hasan, Deema Jaber, Omar F. Khabour

et al.

BMC Medical Ethics, Journal Year: 2024, Volume and Issue: 25(1)

Published: May 16, 2024

Abstract Background Integrating artificial intelligence (AI) into healthcare has raised significant ethical concerns. In pharmacy practice, AI offers promising advances but also poses challenges. Methods A cross-sectional study was conducted in countries from the Middle East and North Africa (MENA) region on 501 professionals. 12-item online questionnaire assessed concerns related to adoption of practice. Demographic factors associated with were analyzed via SPSS v.27 software using appropriate statistical tests. Results Participants expressed about patient data privacy (58.9%), cybersecurity threats potential job displacement (62.9%), lack legal regulation (67.0%). Tech-savviness basic understanding correlated higher concern scores ( p < 0.001). Ethical implications include need for informed consent, beneficence, justice, transparency use AI. Conclusion The findings emphasize importance guidelines, education, autonomy adopting Collaboration, privacy, equitable access are crucial responsible

Language: Английский

Citations

20
Navigating Bias and Fairness in Digital AI Systems DOI
Muhammad Usman Tariq

Advances in human and social aspects of technology book series, Journal Year: 2024, Volume and Issue: unknown, P. 127 - 156

Published: Oct. 17, 2024

In an era where AI advancements permeate various facets of daily life, ranging from healthcare decision-making to personalized content delivery, the potential for biases exacerbate societal inequalities has become a pressing concern. The chapter commences by defining and scrutinizing forms bias in artificial intelligence, elucidating their tangible effects through compelling case studies. Subsequently, it explores theoretical foundations fairness AI, considering conceptual frameworks such as distributive justice procedural while addressing challenges operationalizing these principles. section delves into methods tools identifying measuring datasets algorithms, introducing metrics benchmarks assess outcomes. Strategies best practices mitigating are examined, encompassing approaches data preprocessing, algorithmic adjustments, post-hoc corrections.

Language: Английский

Citations

20
Bias and Class Imbalance in Oncologic Data—Towards Inclusive and Transferrable AI in Large Scale Oncology Data Sets DOI Open Access
Erdal Taşçı, Ying Zhuge, Kevin Camphausen

et al.

Cancers, Journal Year: 2022, Volume and Issue: 14(12), P. 2897 - 2897

Published: June 12, 2022

Recent technological developments have led to an increase in the size and types of data medical field derived from multiple platforms such as proteomic, genomic, imaging, clinical data. Many machine learning models been developed support precision/personalized medicine initiatives computer-aided detection, diagnosis, prognosis, treatment planning by using large-scale Bias class imbalance represent two most pressing challenges for learning-based problems, particularly (e.g., oncologic) sets, due limitations patient numbers, cost, privacy, security sharing, complexity generated Depending on set research question, methods applied address problems can provide more effective, successful, meaningful results. This review discusses essential strategies addressing mitigating different oncologic domain.

Language: Английский

Citations

60
Artificial Intelligence in Drug Discovery and Development DOI
Kit‐Kay Mak,

Yi-Hang Wong,

Mallikarjuna Rao Pichika

et al.

Springer eBooks, Journal Year: 2023, Volume and Issue: unknown, P. 1 - 38

Published: Jan. 1, 2023

Language: Английский

Citations

35
Balancing the scale: navigating ethical and practical challenges of artificial intelligence (AI) integration in legal practices DOI Creative Commons
Ammar Zafar

Discover Artificial Intelligence, Journal Year: 2024, Volume and Issue: 4(1)

Published: April 15, 2024

Abstract The paper explores the integration of artificial intelligence in legal practice, discussing ethical and practical issues that arise how it affects customary procedures. It emphasises shift from labour-intensive practice to technology-enhanced methods, with a focus on intelligence's potential improve access services streamline This discussion importantly highlights challenges introduced by Artificial Intelligence, specific bias transparency. These concerns become particularly paramount context sensitive areas, including but not limited to, child custody disputes, criminal justice, divorce settlements. underscores critical need for maintaining vigilance, advocating developing implementing AI systems characterised profound commitment integrity. approach is vital guarantee fairness uphold transparency across all judicial proceedings. study advocates "human loop" strategy combines human knowledge techniques mitigate biases individualised results ensure functions as complement rather than replacement, concludes emphasising necessity preserving element practices.

Language: Английский

Citations

16
Artificial Intelligence in Drug Discovery and Development DOI
Kit‐Kay Mak,

Yi-Hang Wong,

Mallikarjuna Rao Pichika

et al.

Springer eBooks, Journal Year: 2024, Volume and Issue: unknown, P. 1461 - 1498

Published: Jan. 1, 2024

Language: Английский

Citations

16