Bias in artificial intelligence algorithms and recommendations for mitigation

PLOS Digital Health, Journal Year: 2023, Volume and Issue: 2(6), P. e0000278 - e0000278

Published: June 22, 2023

The adoption of artificial intelligence (AI) algorithms is rapidly increasing in healthcare. Such may be shaped by various factors such as social determinants health that can influence outcomes. While AI have been proposed a tool to expand the reach quality healthcare underserved communities and improve equity, recent literature has raised concerns about propagation biases disparities through implementation these algorithms. Thus, it critical understand sources bias inherent AI-based This review aims highlight potential within each step developing healthcare, starting from framing problem, data collection, preprocessing, development, validation, well their full implementation. For steps, we also discuss strategies mitigate disparities. A checklist was developed with recommendations for reducing during development stages. It important developers users keep considerations mind advance equity all populations.

Language: Английский

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Generative AI in healthcare: an implementation science informed translational path on application, integration and governance

Implementation Science, Journal Year: 2024, Volume and Issue: 19(1)

Published: March 15, 2024

Abstract Background Artificial intelligence (AI), particularly generative AI, has emerged as a transformative tool in healthcare, with the potential to revolutionize clinical decision-making and improve health outcomes. Generative capable of generating new data such text images, holds promise enhancing patient care, revolutionizing disease diagnosis expanding treatment options. However, utility impact AI healthcare remain poorly understood, concerns around ethical medico-legal implications, integration into service delivery workforce utilisation. Also, there is not clear pathway implement integrate delivery. Methods This article aims provide comprehensive overview use focusing on technology its translational application highlighting need for careful planning, execution management expectations adopting medicine. Key considerations include factors privacy, security irreplaceable role clinicians’ expertise. Frameworks like acceptance model (TAM) Non-Adoption, Abandonment, Scale-up, Spread Sustainability (NASSS) are considered promote responsible integration. These frameworks allow anticipating proactively addressing barriers adoption, facilitating stakeholder participation responsibly transitioning care systems harness AI’s potential. Results transform through automated systems, enhanced democratization expertise diagnostic support tools providing timely, personalized suggestions. applications across billing, diagnosis, research can also make more efficient, equitable effective. necessitates meticulous change risk mitigation strategies. Technological capabilities alone cannot shift complex ecosystems overnight; rather, structured adoption programs grounded implementation science imperative. Conclusions It strongly argued this that usher tremendous progress, if introduced responsibly. Strategic based science, incremental deployment balanced messaging opportunities versus limitations helps safe, Extensive real-world piloting iteration aligned priorities should drive development. With conscientious governance centred human wellbeing over technological novelty, enhance accessibility, affordability quality care. As these models continue advancing rapidly, ongoing reassessment transparent communication their strengths weaknesses vital restoring trust, realizing positive and, most importantly, improving

Language: Английский

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Artificial intelligence in nursing and midwifery: A systematic review

Journal of Clinical Nursing, Journal Year: 2022, Volume and Issue: 32(13-14), P. 2951 - 2968

Published: July 31, 2022

Abstract Background Artificial Intelligence (AI) techniques are being applied in nursing and midwifery to improve decision‐making, patient care service delivery. However, an understanding of the real‐world applications AI across all domains both professions is limited. Objectives To synthesise literature on midwifery. Methods CINAHL, Embase, PubMed Scopus were searched using relevant terms. Titles, abstracts full texts screened against eligibility criteria. Data extracted, analysed, findings presented a descriptive summary. The PRISMA checklist guided review conduct reporting. Results One hundred forty articles included. Nurses’ midwives' involvement varied, with some taking active role testing, or evaluating AI‐based technologies; however, many studies did not include either profession. was mainly clinical practice direct ( n = 115, 82.14%), fewer focusing administration management 21, 15.00%), education 4, 2.85%). Benefits reported primarily potential as most trained tested algorithms. Only handful 8, 7.14%) actual benefits when settings. Risks limitations included poor quality datasets that could introduce bias, need for interpretation results, privacy trust issues, inadequate expertise among professions. Conclusion Digital health should be put place support use, evaluation Curricula developed educate about AI, so they can lead participate these digital initiatives healthcare. Relevance Adult, paediatric, mental learning disability nurses, along midwives have more rigorous, interdisciplinary research technologies professional determine their efficacy well ethical, legal social implications

Language: Английский

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Artificial intelligence in ophthalmology: The path to the real-world clinic

Cell Reports Medicine, Journal Year: 2023, Volume and Issue: 4(7), P. 101095 - 101095

Published: June 28, 2023

Artificial intelligence (AI) has great potential to transform healthcare by enhancing the workflow and productivity of clinicians, enabling existing staff serve more patients, improving patient outcomes, reducing health disparities. In field ophthalmology, AI systems have shown performance comparable with or even better than experienced ophthalmologists in tasks such as diabetic retinopathy detection grading. However, despite these quite good results, very few been deployed real-world clinical settings, challenging true value systems. This review provides an overview current main applications describes challenges that need be overcome prior implementation systems, discusses strategies may pave way translation

Language: Английский

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Large Language Models in Healthcare and Medical Domain: A Review

Informatics, Journal Year: 2024, Volume and Issue: 11(3), P. 57 - 57

Published: Aug. 7, 2024

The deployment of large language models (LLMs) within the healthcare sector has sparked both enthusiasm and apprehension. These exhibit remarkable ability to provide proficient responses free-text queries, demonstrating a nuanced understanding professional medical knowledge. This comprehensive survey delves into functionalities existing LLMs designed for applications elucidates trajectory their development, starting with traditional Pretrained Language Models (PLMs) then moving present state in sector. First, we explore potential amplify efficiency effectiveness diverse applications, particularly focusing on clinical tasks. tasks encompass wide spectrum, ranging from named entity recognition relation extraction natural inference, multimodal document classification, question-answering. Additionally, conduct an extensive comparison most recent state-of-the-art domain, while also assessing utilization various open-source highlighting significance applications. Furthermore, essential performance metrics employed evaluate biomedical shedding light limitations. Finally, summarize prominent challenges constraints faced by offering holistic perspective benefits shortcomings. review provides exploration current landscape healthcare, addressing role transforming areas that warrant further research development.

Language: Английский

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Artificial intelligence (AI) and machine learning (ML) based decision support systems in mental health: An integrative review

International Journal of Mental Health Nursing, Journal Year: 2023, Volume and Issue: 32(4), P. 966 - 978

Published: Feb. 6, 2023

Abstract An integrative review investigating the incorporation of artificial intelligence (AI) and machine learning (ML) based decision support systems in mental health care settings was undertaken published literature between 2016 2021 across six databases. Four studies met research question inclusion criteria. The primary theme identified trust confidence . To date, there is limited regarding use AI‐based health. Our found that significant barriers exist its into practice primarily arising from uncertainty related to clinician's confidence, end‐user acceptance system transparency. More needed understand role AI assisting treatment identifying missed care. Researchers developers must focus on establishing with clinical staff before true impact can be determined. Finally, further required attitudes beliefs surrounding impacts for wellbeing end‐users This highlights necessity involving clinicians all stages research, development implementation delivery. Earning should foremost consideration any system. Clinicians motivated actively embrace opportunity contribute new technologies digital tools assist professionals identify care, it occurs as a matter importance public safety ethical implementation. AI‐basesd show most promise achieved.

Language: Английский

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Evaluating large language models for use in healthcare: A framework for translational value assessment

Informatics in Medicine Unlocked, Journal Year: 2023, Volume and Issue: 41, P. 101304 - 101304

Published: Jan. 1, 2023

The recent focus on Large Language Models (LLMs) has yielded unprecedented discussion of their potential use in various domains, including healthcare. While showing considerable performing human-capable tasks, LLMs have also demonstrated significant drawbacks, generating misinformation, falsifying data, and contributing to plagiarism. These aspects are generally concerning but can be more severe the context As explored for utility healthcare, discharge summaries, interpreting medical records providing advice, it is necessary ensure safeguards around Notably, there must an evaluation process that assesses natural language processing performance translational value. Complementing this assessment, a governance layer accountability public confidence such models. Such framework discussed presented paper.

Language: Английский

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The ethical implications of using generative chatbots in higher education

Frontiers in Education, Journal Year: 2024, Volume and Issue: 8

Published: Jan. 8, 2024

Incorporating artificial intelligence (AI) into education, specifically through generative chatbots, can transform teaching and learning for education professionals in both administrative pedagogical ways. However, the ethical implications of using chatbots must be carefully considered. Ethical concerns about advanced have yet to explored sector. This short article introduces associated with introducing platforms such as ChatGPT education. The outlines how handling sensitive student data by presents significant privacy challenges, thus requiring adherence protection regulations, which may not always possible. It highlights risk algorithmic bias could perpetuate societal biases, problematic. also examines balance between fostering autonomy potential impact on academic self-efficacy, noting over-reliance AI educational purposes. Plagiarism continues emerge a critical concern, AI-generated content threatening integrity. advocates comprehensive measures address these issues, including clear policies, plagiarism detection techniques, innovative assessment methods. By addressing argues that educators, developers, policymakers, students fully harness creating more inclusive, empowering, ethically sound future.

Language: Английский

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Transformer Models in Healthcare: A Survey and Thematic Analysis of Potentials, Shortcomings and Risks

Journal of Medical Systems, Journal Year: 2024, Volume and Issue: 48(1)

Published: Feb. 17, 2024

Large Language Models (LLMs) such as General Pretrained Transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT), which use transformer model architectures, have significantly advanced artificial intelligence natural language processing. Recognized for their ability to capture associative relationships between words based on shared context, these models are poised transform healthcare by improving diagnostic accuracy, tailoring treatment plans, predicting patient outcomes. However, there multiple risks potentially unintended consequences associated with in applications. This study, conducted 28 participants using a qualitative approach, explores the benefits, shortcomings, of healthcare. It analyses responses seven open-ended questions simplified thematic analysis. Our research reveals including improved operational efficiency, optimized processes refined clinical documentation. Despite significant concerns about introduction bias, auditability issues privacy risks. Challenges include need specialized expertise, emergence ethical dilemmas potential reduction human element care. For medical profession, impact employment, changes patient-doctor dynamic, extensive training both system operation data interpretation.

Language: Английский

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Value Creation Through Artificial Intelligence and Cardiovascular Imaging: A Scientific Statement From the American Heart Association

Circulation, Journal Year: 2024, Volume and Issue: 149(6)

Published: Jan. 9, 2024

Multiple applications for machine learning and artificial intelligence (AI) in cardiovascular imaging are being proposed developed. However, the processes involved implementing AI highly diverse, varying by modality, patient subtype, features to be extracted analyzed, clinical application. This article establishes a framework that defines value from an organizational perspective, followed chain analysis identify activities which might produce greatest incremental creation. The various perspectives should considered highlighted, including clinicians, imagers, hospitals, patients, payers. Integrating of all health care stakeholders is critical creating ensuring successful deployment tools real-world setting. Different summarized, along with unique aspects cardiac modalities, computed tomography, magnetic resonance imaging, positron emission tomography. applicable has potential add at every step journey, selecting more appropriate test optimizing image acquisition analysis, interpreting results classification diagnosis, predicting risk major adverse events.

Language: Английский

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