Artificial Intelligence in Healthcare: Review, Ethics, Trust Challenges & Future Research Directions

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 120, P. 105894 - 105894

Published: Jan. 28, 2023

Language: Английский

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Artificial intelligence and machine learning in precision medicine: A paradigm shift in big data analysis

Progress in molecular biology and translational science, Journal Year: 2022, Volume and Issue: unknown, P. 57 - 100

Published: Jan. 1, 2022

Language: Английский

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Human-Centered Design to Address Biases in Artificial Intelligence

Journal of Medical Internet Research, Journal Year: 2023, Volume and Issue: 25, P. e43251 - e43251

Published: March 24, 2023

The potential of artificial intelligence (AI) to reduce health care disparities and inequities is recognized, but it can also exacerbate these issues if not implemented in an equitable manner. This perspective identifies biases each stage the AI life cycle, including data collection, annotation, machine learning model development, evaluation, deployment, operationalization, monitoring, feedback integration. To mitigate biases, we suggest involving a diverse group stakeholders, using human-centered principles. Human-centered help ensure that systems are designed used way benefits patients society, which inequities. By recognizing addressing at achieve its care.

Language: Английский

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The dark side of FinTech in financial services: a qualitative enquiry into FinTech developers’ perspective

International Journal of Bank Marketing, Journal Year: 2023, Volume and Issue: 42(1), P. 38 - 65

Published: July 4, 2023

Purpose FinTech offers numerous prospects for significant enhancements and fundamental changes in financial services. However, along with the myriad of benefits, it also has potential to induce risks individuals, organisations society. This study focuses on understanding developers’ perspective dark side FinTech. Design/methodology/approach conducted semi-structured interviews 23 Nigerian developers using an exploratory, inductive methodology The data were transcribed then thematically analysed NVivo. Findings Three themes – customer vulnerability, technical inability regulatory irresponsibility arose from thematic analysis. poor existing technological infrastructure, management challenges, limited access smartphone adoption pose challenges a speedy integration country, making customers vulnerable. lack privacy control leads ethical issues. skilled brain drain good present additional obstacles development Nigeria. Research limitations/implications operation developing country differs that developed countries better infrastructure institutional acceptance. recognises basic banking operations through are still not well adopted, necessitating need be more open-minded about global practicalities Practical implications managers, banks policymakers can ethically collect consumer help influence credit decisions, product recommendations mobile app transaction history. There should strict penalties selling customers’ data, sending unsolicited messages or gaining unnecessary customer’s contact list. offer educate consumers their skills. Originality/value Whereas other studies have focused positive aspects understand client perceptions, this new insights into by analysing viewpoints developers. Furthermore, is based Nigeria, emerging economy adopting FinTech, adding dimension body knowledge.

Language: Английский

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Diverse patients’ attitudes towards Artificial Intelligence (AI) in diagnosis

PLOS Digital Health, Journal Year: 2023, Volume and Issue: 2(5), P. e0000237 - e0000237

Published: May 19, 2023

Artificial intelligence (AI) has the potential to improve diagnostic accuracy. Yet people are often reluctant trust automated systems, and some patient populations may be particularly distrusting. We sought determine how diverse feel about use of AI tools, whether framing informing choice affects uptake. To construct pretest our materials, we conducted structured interviews with a set actual patients. then pre-registered (osf.io/9y26x), randomized, blinded survey experiment in factorial design. A firm provided n = 2675 responses, oversampling minoritized populations. Clinical vignettes were randomly manipulated eight variables two levels each: disease severity (leukemia versus sleep apnea), is proven more accurate than human specialists, clinic personalized through listening and/or tailoring, avoids racial financial biases, Primary Care Physician (PCP) promises explain incorporate advice, PCP nudges towards as established, recommended, easy choice. Our main outcome measure was selection or physician specialist (binary, “AI uptake”). found that weighting representative U.S. population, respondents almost evenly split (52.9% chose doctor 47.1% clinic). In unweighted experimental contrasts who met criteria for engagement, PCP’s explanation superior accuracy increased uptake (OR 1.48, CI 1.24–1.77, p < .001), did nudge established 1.25, CI: 1.05–1.50, .013), reassurance had trained counselors listen patient’s unique perspectives 1.27, 1.07–1.52, .008). Disease apnea) other manipulations not affect significantly. Compared White respondents, Black selected less .73, .55-.96, .023) Native Americans it (OR: 1.37, 1.01–1.87, .041). Older likely choose .99, .987-.999, .03), those identified politically conservative .65, .52-.81, .001) viewed religion important .64, .52-.77, .001). For each unit increase education, odds 1.10 greater selecting an provider 1.10, 1.03–1.18, .004). While many patients appear resistant AI, information, experience help acceptance. ensure benefits secured clinical practice, future research on best methods incorporation decision making required.

Language: Английский

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Artificial Intelligence in Genetics

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

Published: Jan. 10, 2024

The simulation of human intelligence in robots that are designed to think and learn like humans is known as artificial (AI). AI creating a world has never been seen before. By applying do jobs would otherwise take long time, have the chance improve our planet. great potential genetic engineering gene therapy research. powerful tool for new hypotheses helping with experimental techniques. From previous data model, it can help detection heredity gene-related disorders. developments offer an excellent possibility rational drug discovery design, eventually impacting humanity. Drug development depend greatly on machine learning (ML) technology. Genetics not exception this trend, ML expected impact nearly every aspect experience. significantly aided treatment various biomedical conditions, including In both basic applied research, deep - highly versatile branch enables autonomous feature extraction increasingly exploited. review, we cover broad spectrum current uses genetics. enormous field genetics, but its advancement area may be hampered future by lack knowledge about accompanying difficulties could mask any possible benefits patients. This paper examines AI's significance advancing precision disease treatment, provides peek at use clinical care, number existing clinician primer critical aspects these technologies, makes predictions applications illnesses.

Language: Английский

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Unlocking precision medicine: clinical applications of integrating health records, genetics, and immunology through artificial intelligence

Journal of Biomedical Science, Journal Year: 2025, Volume and Issue: 32(1)

Published: Feb. 7, 2025

Abstract Artificial intelligence (AI) has emerged as a transformative force in precision medicine, revolutionizing the integration and analysis of health records, genetics, immunology data. This comprehensive review explores clinical applications AI-driven analytics unlocking personalized insights for patients with autoimmune rheumatic diseases. Through synergistic approach integrating AI across diverse data sets, clinicians gain holistic view patient potential risks. Machine learning models excel at identifying high-risk patients, predicting disease activity, optimizing therapeutic strategies based on clinical, genomic, immunological profiles. Deep techniques have significantly advanced variant calling, pathogenicity prediction, splicing analysis, MHC-peptide binding predictions genetics. AI-enabled including dimensionality reduction, cell population identification, sample classification, provides unprecedented into complex immune responses. The highlights real-world examples medicine platforms decision support tools rheumatology. Evaluation outcomes demonstrates benefits impact these approaches care. However, challenges such quality, privacy, clinician trust must be navigated successful implementation. future lies continued research, development, to unlock care drive innovation

Language: Английский

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Artificial intelligence‐enabled innovations in cochlear implant technology: Advancing auditory prosthetics for hearing restoration

Bioengineering & Translational Medicine, Journal Year: 2025, Volume and Issue: 10(3)

Published: Jan. 9, 2025

This comprehensive review explores the implications of artificial intelligence (AI) in addressing cochlear implant (CI) issues and revolutionizing landscape auditory prosthetics. It begins with an overview ear anatomy hearing loss, then a CI technology its current challenges. The emphasizes how advanced AI algorithms data-driven approaches enhance adaptability functionality, enabling personalized rehabilitation strategies improving speech enhancement. highlights diverse applications rehabilitation, including real-time adaptive control mechanisms cognitive assistants that help users manage their health. By outlining innovative pathways future directions for AI-enhanced CIs, paper sets stage transformative shift prosthetics, aiming to improve quality life individuals loss.

Language: Английский

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Integrating Artificial and Human Intelligence: A Partnership for Responsible Innovation in Biomedical Engineering and Medicine

OMICS A Journal of Integrative Biology, Journal Year: 2019, Volume and Issue: 24(5), P. 247 - 263

Published: July 17, 2019

Historically, the term "artificial intelligence" dates to 1956 when it was first used in a conference at Dartmouth College US. Since then, development of artificial intelligence has part been shaped by field neuroscience. By understanding human brain, scientists have attempted build new intelligent machines capable performing complex tasks akin humans. Indeed, future research into will continue benefit from study brain. While algorithms fast paced, actual use most (AI) biomedical engineering and clinical practice is still markedly below its conceivably broader potentials. This partly because for any algorithm be incorporated existing workflows stand test scientific validation, personal utility, application context, equitable as well. In this there much gained combining AI (HI). Harnessing Big Data, computing power storage capacities, addressing societal issues emergent applications, demand deploying HI tandem with AI. Very few countries, even economically developed states, lack adequate critical governance frames best understand steer innovation trajectories health care. Drug discovery translational pharmaceutical gain technology provided they are also informed HI. expert review, we analyze ways which applications likely traverse continuum life birth death, encompassing not only humans but all animal, plant, other living organisms that increasingly touched Examples include digital health, diagnosis diseases newborns, remote monitoring smart devices, real-time Data analytics prompt heart attacks, facial analysis software consequences on civil liberties. underscore need integration HI, note does replace medical specialists or rather, such Altogether, offer synergy responsible veritable prospects improving care prevention therapeutics while unintended automation should borne mind cultures, work force, society large.

Language: Английский

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Toward Clinical Implementation of Next-Generation Sequencing-Based Genetic Testing in Rare Diseases: Where Are We?

Trends in Genetics, Journal Year: 2019, Volume and Issue: 35(11), P. 852 - 867

Published: Oct. 14, 2019

Next-generation sequencing (NGS) technologies have changed the landscape of genetic testing in rare diseases. However, rapid evolution NGS has outpaced its clinical adoption. Here, we re-evaluate critical steps application NGS-based from an informatics perspective. We suggest a 'fit-for-purpose' triage current technologies. also point out potential shortcomings management variants and offer ideas for improvement. specifically emphasize importance ensuring accuracy reproducibility context disease diagnosis. highlight role artificial intelligence (AI) enhancing understanding prioritization variance setting propose deep learning frameworks further investigation.

Language: Английский

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