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

Molecular Diversity, Год журнала: 2021, Номер 26(3), С. 1893 - 1913

Опубликована: Окт. 23, 2021

Язык: Английский

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Patients’ Perceptions Toward Human–Artificial Intelligence Interaction in Health Care: Experimental Study

Journal of Medical Internet Research, Год журнала: 2021, Номер 23(11), С. e25856 - e25856

Опубликована: Ноя. 25, 2021

It is believed that artificial intelligence (AI) will be an integral part of health care services in the near future and incorporated into several aspects clinical such as prognosis, diagnostics, planning. Thus, many technology companies have invested producing AI applications. Patients are one most important beneficiaries who potentially interact with these technologies applications; thus, patients' perceptions may affect widespread use AI. should ensured applications not harm them, they instead benefit from using for purposes. Although human-AI interaction can enhance outcomes, possible dimensions concerns risks addressed before its integration routine care.The main objective this study was to examine how potential users (patients) perceive benefits, risks, their purposes different if faced three service encounter scenarios.We designed a 2×3 experiment crossed type condition (ie, acute or chronic) types encounters between patients physicians substituting technology, augmenting no traditional in-person visit). We used online survey collect data 634 individuals United States.The interactions conditions significantly influenced individuals' privacy concerns, trust issues, communication barriers, about transparency regulatory standards, liability intention across six scenarios. found significant differences among scenarios regarding performance risk social biases.The results imply incompatibility instrumental, technical, ethical, values reason rejecting care. there still various associated implementing diagnostics treatment recommendations both chronic illnesses. The also evident recommendation system under physician experience, wisdom, control. Prior rollout AI, more studies needed identify challenges raise This could provide researchers managers critical insights determinants Regulatory agencies establish normative standards evaluation guidelines cooperation institutions. Regular audits ongoing monitoring reporting systems continuously evaluate safety, quality, transparency, ethical factors

Язык: Английский

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Artificial Intelligence Solutions to Increase Medication Adherence in Patients With Non-communicable Diseases

Frontiers in Digital Health, Год журнала: 2021, Номер 3

Опубликована: Июнь 29, 2021

Artificial intelligence (AI) tools are increasingly being used within healthcare for various purposes, including helping patients to adhere drug regimens. The aim of this narrative review was describe: (1) studies on AI that can be measure and increase medication adherence in with non-communicable diseases (NCDs); (2) the benefits using these purposes; (3) challenges use healthcare; (4) priorities future research. We discuss current technologies, mobile phone applications, reminder systems, patient empowerment, instruments integrated care, machine learning. may key understanding complex interplay factors underly non-adherence NCD patients. AI-assisted interventions aiming improve communication between physicians, monitor consumption, empower patients, ultimately, levels lead better clinical outcomes quality life However, is challenged by numerous factors; characteristics users impact effectiveness an tool, which further inequalities healthcare, there concerns it could depersonalize medicine. success widespread technologies will depend data storage capacity, processing power, other infrastructure capacities systems. Research needed evaluate solutions different groups establish barriers adoption, especially light COVID-19 pandemic, has led a rapid development digital health technologies.

Язык: Английский

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Exploring stakeholder attitudes towards AI in clinical practice

BMJ Health & Care Informatics, Год журнала: 2021, Номер 28(1), С. e100450 - e100450

Опубликована: Дек. 1, 2021

Objectives Different stakeholders may hold varying attitudes towards artificial intelligence (AI) applications in healthcare, which constrain their acceptance if AI developers fail to take them into account. We set out ascertain evidence of the clinicians, consumers, managers, researchers, regulators and industry healthcare. Methods undertook an exploratory analysis articles whose titles or abstracts contained terms ‘artificial intelligence’ ‘AI’ ‘medical’ ‘healthcare’ ‘attitudes’, ‘perceptions’, ‘opinions’, ‘views’, ‘expectations’. Using a snowballing strategy, we searched PubMed Google Scholar for published 1 January 2010 through 31 May 2021. selected relating non-robotic clinician-facing used support healthcare-related tasks decision-making. Results Across 27 studies, general, were positive, more so those with direct experience AI, but provided certain safeguards met. automated data interpretation synthesis regarded favourably by clinicians consumers than that directly influenced clinical decisions potentially impacted clinician–patient relationships. Privacy breaches personal liability AI-related error worried while loss clinician oversight inability fully share decision-making consumers. Both wanted AI-generated advice be trustworthy, groups emphasised benefits data, funding regulatory certainty. Discussion Certain expectations common many stakeholder from dependencies can defined. Conclusion Stakeholders differ some not all AI. Those developing implementing should consider policies processes bridge attitudinal disconnects between different stakeholders.

Язык: Английский

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Artificial intelligence (AI)-enabled CRM capability in healthcare: The impact on service innovation

International Journal of Information Management, Год журнала: 2022, Номер 69, С. 102598 - 102598

Опубликована: Ноя. 28, 2022

Язык: Английский

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A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?

IEEE Transactions on Artificial Intelligence, Год журнала: 2023, Номер 5(4), С. 1429 - 1442

Опубликована: Апрель 13, 2023

Artificial intelligence (AI) models are increasingly finding applications in the field of medicine. Concerns have been raised about explainability decisions that made by these AI models. In this article, we give a systematic analysis explainable artificial (XAI), with primary focus on currently being used healthcare. The literature search is conducted following preferred reporting items for reviews and meta-analyses (PRISMA) standards relevant work published from 1 January 2012 to 02 February 2022. review analyzes prevailing trends XAI lays out major directions which research headed. We investigate why, how, when uses their implications. present comprehensive examination methodologies as well an explanation how trustworthy can be derived describing healthcare fields. discussion will contribute formalization field.

Язык: Английский

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Challenges and strategies for wide-scale artificial intelligence (AI) deployment in healthcare practices: A perspective for healthcare organizations

Artificial Intelligence in Medicine, Год журнала: 2024, Номер 151, С. 102861 - 102861

Опубликована: Март 30, 2024

Язык: Английский

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Smart Health

Journal of Organizational and End User Computing, Год журнала: 2022, Номер 34(1), С. 1 - 14

Опубликована: Авг. 11, 2022

In recent decades, healthcare organizations around the world have increasingly appreciated value of information technologies for a variety applications. Three new technological advancements that are impacting smart health metaverse, artificial intelligence (AI), and data science. The metaverse is intersection three major — AI, augmented reality (AR), virtual (VR). Metaverse provides possibilities potential still emerging. increased work efficiency enabled by science in hospitals not only improves patient care but also cuts costs workload providers. Artificial intelligence, coupled with machine learning, transforming industry. availability big enables scientists to use descriptive, predictive, prescriptive analytics. This article reviews multiple case studies literature on AI applications hospital administration. presents unresolved research questions challenges context. For researchers, addition providing good synopsis development area, this identifies possible future directions discusses health. practitioners, both decision-makers workers practical guidelines management model.

Язык: Английский

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Medical artificial intelligence ethics: A systematic review of empirical studies

Digital Health, Год журнала: 2023, Номер 9

Опубликована: Янв. 1, 2023

Background Artificial intelligence (AI) technologies are transforming medicine and healthcare. Scholars practitioners have debated the philosophical, ethical, legal, regulatory implications of medical AI, empirical research on stakeholders’ knowledge, attitude, practices has started to emerge. This study is a systematic review published studies AI ethics with goal mapping main approaches, findings, limitations scholarship inform future practice considerations. Methods We searched seven databases for peer-reviewed evaluated them in terms types studied, geographic locations, stakeholders involved, methods used, ethical principles major findings. Findings Thirty-six were included (published 2013-2022). They typically belonged one three topics: exploratory stakeholder knowledge attitude toward theory-building testing hypotheses regarding factors contributing acceptance identifying correcting bias AI. Interpretation There disconnect between high-level guidelines developed by ethicists topic need embed tandem developers, clinicians, patients, scholars innovation technology adoption studying ethics.

Язык: Английский

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The Utility of Language Models in Cardiology: A Narrative Review of the Benefits and Concerns of ChatGPT-4

International Journal of Environmental Research and Public Health, Год журнала: 2023, Номер 20(15), С. 6438 - 6438

Опубликована: Июль 25, 2023

Artificial intelligence (AI) and language models such as ChatGPT-4 (Generative Pretrained Transformer) have made tremendous advances recently are rapidly transforming the landscape of medicine. Cardiology is among many specialties that utilize AI with intention improving patient care. Generative AI, use its advanced machine learning algorithms, has potential to diagnose heart disease recommend management options suitable for patient. This may lead improved outcomes not only by recommending best treatment plan but also increasing physician efficiency. Language could assist physicians administrative tasks, allowing them spend more time on However, there several concerns in field These technologies be most up-to-date latest research provide outdated information, which an adverse event. Secondly, tools can expensive, leading increased healthcare costs reduced accessibility general population. There concern about loss human touch empathy becomes mainstream. Healthcare professionals would need adequately trained these tools. While beneficial traits, all providers involved aware generative so assure optimal mitigate any risks challenges associated implementation. In this review, we discuss various uses cardiology.

Язык: Английский

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