Exploring stakeholder attitudes towards AI in clinical practice

Being diagnosed with a rhabdomyosarcoma in the era of artificial intelligence: Whom can we trust?

In the era of big data, young patients may be overwhelmed by artificial intelligence-based tools, like chatbots. Five clinical experts were asked to evaluate the performance of the most currently used chatbots in providing information on a rare cancer affecting young people, like rhabdomyosarcoma. Generally speaking, despite their high performance in giving general information about the disease, these chatbots were considered by the experts to be inadequate in providing suggestions on cancer treatments and specialized centers, and also lacking in "sensitivity." Efforts are planned by the pediatric oncology community to improve the quality of data used to train these tools.

Impact of response bias in three surveys on primary care providers' experiences with electronic health records

OBJECTIVE

To identify impacts of different survey methodologies assessing primary care physicians' (PCPs') experiences with electronic health records (EHRs), we compared three surveys: the 2022 Continuous Certification Questionnaire (CCQ) from the American Board of Family Medicine, the 2022 University of California San Francisco (UCSF) Physician Health IT Survey, and the 2021 National Electronic Health Records Survey (NEHRS).

MATERIALS AND METHODS

We evaluated differences between survey pairs using Rao-Scott corrected chi-square tests, which account for weighting.

RESULTS

CCQ received 3991 responses from PCPs (100% response rate), UCSF received 1375 (3.6% response rate), and NEHRS received 858 (18.2% response rate). Substantial, statistically significant differences in demographics were detected across the surveys. CCQ respondents were younger and more likely to work in a health system; NEHRS respondents were more likely to work in private practice; and UCSF respondents disproportionately practiced in larger academic settings. Many EHR experience indicators were similar between CCQ and NEHRS, but CCQ respondents reported higher documentation burden.

DISCUSSION

The UCSF approach is unlikely to supply reliable data. Significant demographic differences between CCQ and NEHRS raise response bias concerns, and while there were similarities in some reported EHR experiences, there were important, significant differences.

CONCLUSION

Federal EHR policy monitoring and maintenance require reliable data. This test of existing and alternative sources suggest that diversified data sources are necessary to understand physicians' experiences with EHRs and interoperability. Comprehensive surveys administered by specialty boards have the potential to contribute to these efforts, since they are likely to be free of response bias.

Achieving large-scale clinician adoption of AI-enabled decision support

Computerised decision support (CDS) tools enabled by artificial intelligence (AI) seek to enhance accuracy and efficiency of clinician decision-making at the point of care. Statistical models developed using machine learning (ML) underpin most current tools. However, despite thousands of models and hundreds of regulator-approved tools internationally, large-scale uptake into routine clinical practice has proved elusive. While underdeveloped system readiness and investment in AI/ML within Australia and perhaps other countries are impediments, clinician ambivalence towards adopting these tools at scale could be a major inhibitor. We propose a set of principles and several strategic enablers for obtaining broad clinician acceptance of AI/ML-enabled CDS tools.

Artificial intelligence predictive analytics in heart failure: results of the pilot phase of a pragmatic randomized clinical trial

OBJECTIVES

We conducted an implementation planning process during the pilot phase of a pragmatic trial, which tests an intervention guided by artificial intelligence (AI) analytics sourced from noninvasive monitoring data in heart failure patients (LINK-HF2).

MATERIALS AND METHODS

A mixed-method analysis was conducted at 2 pilot sites. Interviews were conducted with 12 of 27 enrolled patients and with 13 participating clinicians. iPARIHS constructs were used for interview construction to identify workflow, communication patterns, and clinician's beliefs. Interviews were transcribed and analyzed using inductive coding protocols to identify key themes. Behavioral response data from the AI-generated notifications were collected.

RESULTS

Clinicians responded to notifications within 24 hours in 95% of instances, with 26.7% resulting in clinical action. Four implementation themes emerged: (1) High anticipatory expectations for reliable patient communications, reduced patient burden, and less proactive provider monitoring. (2) The AI notifications required a differential and tailored balance of trust and action advice related to role. (3) Clinic experience with other home-based programs influenced utilization. (4) Responding to notifications involved significant effort, including electronic health record (EHR) review, patient contact, and consultation with other clinicians.

DISCUSSION

Clinician's use of AI data is a function of beliefs regarding the trustworthiness and usefulness of the data, the degree of autonomy in professional roles, and the cognitive effort involved.

CONCLUSION

The implementation planning analysis guided development of strategies that addressed communication technology, patient education, and EHR integration to reduce clinician and patient burden in the subsequent main randomized phase of the trial. Our results provide important insights into the unique implications of implementing AI analytics into clinical workflow.

Patient Perspectives on the Use of Artificial Intelligence in Health Care: A Scoping Review

Purpose

Artificial intelligence (AI) technology is being rapidly adopted into many different branches of medicine. Although research has started to highlight the impact of AI on health care, the focus on patient perspectives of AI is scarce. This scoping review aimed to explore the literature on adult patients' perspectives on the use of an array of AI technologies in the health care setting for design and deployment.

Methods

This scoping review followed Arksey and O'Malley's framework and Preferred Reporting Items for Systematic Reviews and Meta-Analysis for Scoping Reviews (PRISMA-ScR). To evaluate patient perspectives, we conducted a comprehensive literature search using eight interdisciplinary electronic databases, including grey literature. Articles published from 2015 to 2022 that focused on patient views regarding AI technology in health care were included. Thematic analysis was performed on the extracted articles.

Results

Of the 10,571 imported studies, 37 articles were included and extracted. From the 33 peer-reviewed and 4 grey literature articles, the following themes on AI emerged: (i) Patient attitudes, (ii) Influences on patient attitudes, (iii) Considerations for design, and (iv) Considerations for use.

Conclusions

Patients are key stakeholders essential to the uptake of AI in health care. The findings indicate that patients' needs and expectations are not fully considered in the application of AI in health care. Therefore, there is a need for patient voices in the development of AI in health care.

Attitudes towards artificial intelligence in emergency medicine

OBJECTIVE

To assess Australian and New Zealand emergency clinicians' attitudes towards the use of artificial intelligence (AI) in emergency medicine.

METHODS

We undertook a qualitative interview-based study based on grounded theory. Participants were recruited through ED internal mailing lists, the Australasian College for Emergency Medicine Bulletin, and the research teams' personal networks. Interviews were transcribed, coded and themes presented.

RESULTS

Twenty-five interviews were conducted between July 2021 and May 2022. Thematic saturation was achieved after 22 interviews. Most participants were from either Western Australia (52%) or Victoria (16%) and were consultants (96%). More participants reported feeling optimistic (10/25) than neutral (6/25), pessimistic (2/25) or mixed (7/25) towards the use of AI in the ED. A minority expressed scepticism regarding the feasibility or value of implementing AI into the ED. Multiple potential risks and ethical issues were discussed by participants including skill loss from overreliance on AI, algorithmic bias, patient privacy and concerns over liability. Participants also discussed perceived inadequacies in existing information technology systems. Participants felt that AI technologies would be used as decision support tools and not replace the roles of emergency clinicians. Participants were not concerned about the impact of AI on their job security. Most (17/25) participants thought that AI would impact emergency medicine within the next 10 years.

CONCLUSIONS

Emergency clinicians interviewed were generally optimistic about the use of AI in emergency medicine, so long as it is used as a decision support tool and they maintain the ability to override its recommendations.

Designing explainable AI to improve human-AI team performance: A medical stakeholder-driven scoping review

The rise of complex AI systems in healthcare and other sectors has led to a growing area of research called Explainable AI (XAI) designed to increase transparency. In this area, quantitative and qualitative studies focus on improving user trust and task performance by providing system- and prediction-level XAI features. We analyze stakeholder engagement events (interviews and workshops) on the use of AI for kidney transplantation. From this we identify themes which we use to frame a scoping literature review on current XAI features. The stakeholder engagement process lasted over nine months covering three stakeholder group's workflows, determining where AI could intervene and assessing a mock XAI decision support system. Based on the stakeholder engagement, we identify four major themes relevant to designing XAI systems - 1) use of AI predictions, 2) information included in AI predictions, 3) personalization of AI predictions for individual differences, and 4) customizing AI predictions for specific cases. Using these themes, our scoping literature review finds that providing AI predictions before, during, or after decision-making could be beneficial depending on the complexity of the stakeholder's task. Additionally, expert stakeholders like surgeons prefer minimal to no XAI features, AI prediction, and uncertainty estimates for easy use cases. However, almost all stakeholders prefer to have optional XAI features to review when needed, especially in hard-to-predict cases. The literature also suggests that providing both system- and prediction-level information is necessary to build the user's mental model of the system appropriately. Although XAI features improve users' trust in the system, human-AI team performance is not always enhanced. Overall, stakeholders prefer to have agency over the XAI interface to control the level of information based on their needs and task complexity. We conclude with suggestions for future research, especially on customizing XAI features based on preferences and tasks.

Artificial intelligence and machine learning for clinical pharmacology

Artificial intelligence (AI) will impact many aspects of clinical pharmacology, including drug discovery and development, clinical trials, personalized medicine, pharmacogenomics, pharmacovigilance and clinical toxicology. The rapid progress of AI in healthcare means clinical pharmacologists should have an understanding of AI and its implementation in clinical practice. As with any new therapy or health technology, it is imperative that AI tools are subject to robust and stringent evaluation to ensure that they enhance clinical practice in a safe and equitable manner. This review serves as an introduction to AI for the clinical pharmacologist, highlighting current applications, aspects of model development and issues surrounding evaluation and deployment. The aim of this article is to empower clinical pharmacologists to embrace and lead on the safe and effective use of AI within healthcare.

Ethics of artificial intelligence in prenatal and pediatric genomic medicine

This paper examines the ethics of introducing emerging forms of artificial intelligence (AI) into prenatal and pediatric genomic medicine. Application of genomic AI to these early life settings has not received much attention in the ethics literature. We focus on three contexts: (1) prenatal genomic sequencing for possible fetal abnormalities, (2) rapid genomic sequencing for critically ill children, and (3) reanalysis of genomic data obtained from children for diagnostic purposes. The paper identifies and discusses various ethical issues in the possible application of genomic AI in these settings, especially as they relate to concepts of beneficence, nonmaleficence, respect for autonomy, justice, transparency, accountability, privacy, and trust. The examination will inform the ethically sound introduction of genomic AI in early human life.

Diabetes and artificial intelligence beyond the closed loop: a review of the landscape, promise and challenges

The discourse amongst diabetes specialists and academics regarding technology and artificial intelligence (AI) typically centres around the 10% of people with diabetes who have type 1 diabetes, focusing on glucose sensors, insulin pumps and, increasingly, closed-loop systems. This focus is reflected in conference topics, strategy documents, technology appraisals and funding streams. What is often overlooked is the wider application of data and AI, as demonstrated through published literature and emerging marketplace products, that offers promising avenues for enhanced clinical care, health-service efficiency and cost-effectiveness. This review provides an overview of AI techniques and explores the use and potential of AI and data-driven systems in a broad context, covering all diabetes types, encompassing: (1) patient education and self-management; (2) clinical decision support systems and predictive analytics, including diagnostic support, treatment and screening advice, complications prediction; and (3) the use of multimodal data, such as imaging or genetic data. The review provides a perspective on how data- and AI-driven systems could transform diabetes care in the coming years and how they could be integrated into daily clinical practice. We discuss evidence for benefits and potential harms, and consider existing barriers to scalable adoption, including challenges related to data availability and exchange, health inequality, clinician hesitancy and regulation. Stakeholders, including clinicians, academics, commissioners, policymakers and those with lived experience, must proactively collaborate to realise the potential benefits that AI-supported diabetes care could bring, whilst mitigating risk and navigating the challenges along the way.

Perceptions on artificial intelligence-based decision-making for coexisting multiple long-term health conditions: protocol for a qualitative study with patients and healthcare professionals

INTRODUCTION

Coexisting multiple health conditions is common among older people, a population that is increasing globally. The potential for polypharmacy, adverse events, drug interactions and development of additional health conditions complicates prescribing decisions for these patients. Artificial intelligence (AI)-generated decision-making tools may help guide clinical decisions in the context of multiple health conditions, by determining which of the multiple medication options is best. This study aims to explore the perceptions of healthcare professionals (HCPs) and patients on the use of AI in the management of multiple health conditions.

METHODS AND ANALYSIS

A qualitative study will be conducted using semistructured interviews. Adults (≥18 years) with multiple health conditions living in the West Midlands of England and HCPs with experience in caring for patients with multiple health conditions will be eligible and purposively sampled. Patients will be identified from Clinical Practice Research Datalink (CPRD) Aurum; CPRD will contact general practitioners who will in turn, send a letter to patients inviting them to take part. Eligible HCPs will be recruited through British HCP bodies and known contacts. Up to 30 patients and 30 HCPs will be recruited, until data saturation is achieved. Interviews will be in-person or virtual, audio recorded and transcribed verbatim. The topic guide is designed to explore participants' attitudes towards AI-informed clinical decision-making to augment clinician-directed decision-making, the perceived advantages and disadvantages of both methods and attitudes towards risk management. Case vignettes comprising a common decision pathway for patients with multiple health conditions will be presented during each interview to invite participants' opinions on how their experiences compare. Data will be analysed thematically using the Framework Method.

ETHICS AND DISSEMINATION

This study has been approved by the National Health Service Research Ethics Committee (Reference: 22/SC/0210). Written informed consent or verbal consent will be obtained prior to each interview. The findings from this study will be disseminated through peer-reviewed publications, conferences and lay summaries.

Are the European reference networks for rare diseases ready to embrace machine learning? A mixed-methods study

BACKGROUND

The delay in diagnosis for rare disease (RD) patients is often longer than for patients with common diseases. Machine learning (ML) technologies have the potential to speed up and increase the precision of diagnosis in this population group. We aim to explore the expectations and experiences of the members of the European Reference Networks (ERNs) for RDs with those technologies and their potential for application.

METHODS

We used a mixed-methods approach with an online survey followed by a focus group discussion. Our study targeted primarily medical professionals but also other individuals affiliated with any of the 24 ERNs.

RESULTS

The online survey yielded 423 responses from ERN members. Participants reported a limited degree of knowledge of and experience with ML technologies. They considered improved diagnostic accuracy the most important potential benefit, closely followed by the synthesis of clinical information, and indicated the lack of training in these new technologies, which hinders adoption and implementation in routine care. Most respondents supported the option that ML should be an optional but recommended part of the diagnostic process for RDs. Most ERN members saw the use of ML limited to specialised units only in the next 5 years, where those technologies should be funded by public sources. Focus group discussions concluded that the potential of ML technologies is substantial and confirmed that the technologies will have an important impact on healthcare and RDs in particular. As ML technologies are not the core competency of health care professionals, participants deemed a close collaboration with developers necessary to ensure that results are valid and reliable. However, based on our results, we call for more research to understand other stakeholders' opinions and expectations, including the views of patient organisations.

CONCLUSIONS

We found enthusiasm to implement and apply ML technologies, especially diagnostic tools in the field of RDs, despite the perceived lack of experience. Early dialogue and collaboration between health care professionals, developers, industry, policymakers, and patient associations seem to be crucial to building trust, improving performance, and ultimately increasing the willingness to accept diagnostics based on ML technologies.

Integrating ethics in AI development: a qualitative study

BACKGROUND

While the theoretical benefits and harms of Artificial Intelligence (AI) have been widely discussed in academic literature, empirical evidence remains elusive regarding the practical ethical challenges of developing AI for healthcare. Bridging the gap between theory and practice is an essential step in understanding how to ethically align AI for healthcare. Therefore, this research examines the concerns and challenges perceived by experts in developing ethical AI that addresses the healthcare context and needs.

METHODS

We conducted semi-structured interviews with 41 AI experts and analyzed the data using reflective thematic analysis.

RESULTS

We developed three themes that expressed the considerations perceived by experts as essential for ensuring AI aligns with ethical practices within healthcare. The first theme explores the ethical significance of introducing AI with a clear and purposeful objective. The second theme focuses on how experts are concerned about the tension that exists between economic incentives and the importance of prioritizing the interests of doctors and patients. The third theme illustrates the need to develop context-sensitive AI for healthcare that is informed by its underlying theoretical foundations.

CONCLUSIONS

The three themes collectively emphasized that beyond being innovative, AI must genuinely benefit healthcare and its stakeholders, meaning AI also aligns with intricate and context-specific healthcare practices. Our findings signal that instead of narrow product-specific AI guidance, ethical AI development may need a systemic, proactive perspective that includes the ethical considerations (objectives, actors, and context) and focuses on healthcare applications. Ethically developing AI involves a complex interplay between AI, ethics, healthcare, and multiple stakeholders.

Transforming neonatal care with artificial intelligence: challenges, ethical consideration, and opportunities

Artificial intelligence (AI) offers tremendous potential to transform neonatology through improved diagnostics, personalized treatments, and earlier prevention of complications. However, there are many challenges to address before AI is ready for clinical practice. This review defines key AI concepts and discusses ethical considerations and implicit biases associated with AI. Next we will review literature examples of AI already being explored in neonatology research and we will suggest future potentials for AI work. Examples discussed in this article include predicting outcomes such as sepsis, optimizing oxygen therapy, and image analysis to detect brain injury and retinopathy of prematurity. Realizing AI's potential necessitates collaboration between diverse stakeholders across the entire process of incorporating AI tools in the NICU to address testability, usability, bias, and transparency. With multi-center and multi-disciplinary collaboration, AI holds tremendous potential to transform the future of neonatology.

Cognitive Screening for Mild Cognitive Impairment: Clinician Perspectives on Current Practices and Future Directions

 This study surveyed 51 specialist clinicians for their views on existing cognitive screening tests for mild cognitive impairment and their opinions about a hypothetical remote screener driven by artificial intelligence (AI). Responses revealed significant concerns regarding the sensitivity, specificity, and time taken to administer current tests, along with a general willingness to consider adopting telephone-based screening driven by AI. Findings highlight the need to design screeners that address the challenges of recognizing the earliest stages of cognitive decline and that prioritize not only accuracy but also stakeholder input.

Existing Barriers Faced by and Future Design Recommendations for Direct-to-Consumer Health Care Artificial Intelligence Apps: Scoping Review

BACKGROUND

Direct-to-consumer (DTC) health care artificial intelligence (AI) apps hold the potential to bridge the spatial and temporal disparities in health care resources, but they also come with individual and societal risks due to AI errors. Furthermore, the manner in which consumers interact directly with health care AI is reshaping traditional physician-patient relationships. However, the academic community lacks a systematic comprehension of the research overview for such apps.

OBJECTIVE

This paper systematically delineated and analyzed the characteristics of included studies, identified existing barriers and design recommendations for DTC health care AI apps mentioned in the literature and also provided a reference for future design and development.

METHODS

This scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines and was conducted according to Arksey and O'Malley's 5-stage framework. Peer-reviewed papers on DTC health care AI apps published until March 27, 2023, in Web of Science, Scopus, the ACM Digital Library, IEEE Xplore, PubMed, and Google Scholar were included. The papers were analyzed using Braun and Clarke's reflective thematic analysis approach.

RESULTS

Of the 2898 papers retrieved, 32 (1.1%) covering this emerging field were included. The included papers were recently published (2018-2023), and most (23/32, 72%) were from developed countries. The medical field was mostly general practice (8/32, 25%). In terms of users and functionalities, some apps were designed solely for single-consumer groups (24/32, 75%), offering disease diagnosis (14/32, 44%), health self-management (8/32, 25%), and health care information inquiry (4/32, 13%). Other apps connected to physicians (5/32, 16%), family members (1/32, 3%), nursing staff (1/32, 3%), and health care departments (2/32, 6%), generally to alert these groups to abnormal conditions of consumer users. In addition, 8 barriers and 6 design recommendations related to DTC health care AI apps were identified. Some more subtle obstacles that are particularly worth noting and corresponding design recommendations in consumer-facing health care AI systems, including enhancing human-centered explainability, establishing calibrated trust and addressing overtrust, demonstrating empathy in AI, improving the specialization of consumer-grade products, and expanding the diversity of the test population, were further discussed.

CONCLUSIONS

The booming DTC health care AI apps present both risks and opportunities, which highlights the need to explore their current status. This paper systematically summarized and sorted the characteristics of the included studies, identified existing barriers faced by, and made future design recommendations for such apps. To the best of our knowledge, this is the first study to systematically summarize and categorize academic research on these apps. Future studies conducting the design and development of such systems could refer to the results of this study, which is crucial to improve the health care services provided by DTC health care AI apps.

Medical practitioner perspectives on AI in emergency triage

Introduction

A proposed Diagnostic AI System for Robot-Assisted Triage ("DAISY") is under development to support Emergency Department ("ED") triage following increasing reports of overcrowding and shortage of staff in ED care experienced within National Health Service, England ("NHS") but also globally. DAISY aims to reduce ED patient wait times and medical practitioner overload. The objective of this study was to explore NHS health practitioners' perspectives and attitudes towards the future use of AI-supported technologies in ED triage.

Methods

Between July and August 2022 a qualitative-exploratory research study was conducted to collect and capture the perceptions and attitudes of nine NHS healthcare practitioners to better understand the challenges and benefits of a DAISY deployment. The study was based on a thematic analysis of semi-structured interviews. The study involved qualitative data analysis of the interviewees' responses. Audio-recordings were transcribed verbatim, and notes included into data documents. The transcripts were coded line-by-line, and data were organised into themes and sub-themes. Both inductive and deductive approaches to thematic analysis were used to analyse such data.

Results

Based on a qualitative analysis of coded interviews with the practitioners, responses were categorised into broad main thematic-types, namely: trust; current practice; social, legal, ethical, and cultural concerns; and empathetic practice. Sub-themes were identified for each main theme. Further quantitative analyses explored the vocabulary and sentiments of the participants when talking generally about NHS ED practices compared to discussing DAISY. Limitations include a small sample size and the requirement that research participants imagine a prototype AI-supported system still under development. The expectation is that such a system would work alongside the practitioner. Findings can be generalisable to other healthcare AI-supported systems and to other domains.

Discussion

This study highlights the benefits and challenges for an AI-supported triage healthcare solution. The study shows that most NHS ED practitioners interviewed were positive about such adoption. Benefits cited were a reduction in patient wait times in the ED, assistance in the streamlining of the triage process, support in calling for appropriate diagnostics and for further patient examination, and identification of those very unwell and requiring more immediate and urgent attention. Words used to describe the system were that DAISY is a "good idea", "help", helpful, "easier", "value", and "accurate". Our study demonstrates that trust in the system is a significant driver of use and a potential barrier to adoption. Participants emphasised social, legal, ethical, and cultural considerations and barriers to DAISY adoption and the importance of empathy and non-verbal cues in patient interactions. Findings demonstrate how DAISY might support and augment human medical performance in ED care, and provide an understanding of attitudinal barriers and considerations for the development and implementation of future triage AI-supported systems.

Multi-stakeholder preferences for the use of artificial intelligence in healthcare: A systematic review and thematic analysis

INTRODUCTION

Despite the proliferation of Artificial Intelligence (AI) technology over the last decade, clinician, patient, and public perceptions of its use in healthcare raise a number of ethical, legal and social questions. We systematically review the literature on attitudes towards the use of AI in healthcare from patients, the general public and health professionals' perspectives to understand these issues from multiple perspectives.

METHODOLOGY

A search for original research articles using qualitative, quantitative, and mixed methods published between 1 Jan 2001 to 24 Aug 2021 was conducted on six bibliographic databases. Data were extracted and classified into different themes representing views on: (i) knowledge and familiarity of AI, (ii) AI benefits, risks, and challenges, (iii) AI acceptability, (iv) AI development, (v) AI implementation, (vi) AI regulations, and (vii) Human - AI relationship.

RESULTS

The final search identified 7,490 different records of which 105 publications were selected based on predefined inclusion/exclusion criteria. While the majority of patients, the general public and health professionals generally had a positive attitude towards the use of AI in healthcare, all groups indicated some perceived risks and challenges. Commonly perceived risks included data privacy; reduced professional autonomy; algorithmic bias; healthcare inequities; and greater burnout to acquire AI-related skills. While patients had mixed opinions on whether healthcare workers suffer from job loss due to the use of AI, health professionals strongly indicated that AI would not be able to completely replace them in their professions. Both groups shared similar doubts about AI's ability to deliver empathic care. The need for AI validation, transparency, explainability, and patient and clinical involvement in the development of AI was emphasised. To help successfully implement AI in health care, most participants envisioned that an investment in training and education campaigns was necessary, especially for health professionals. Lack of familiarity, lack of trust, and regulatory uncertainties were identified as factors hindering AI implementation. Regarding AI regulations, key themes included data access and data privacy. While the general public and patients exhibited a willingness to share anonymised data for AI development, there remained concerns about sharing data with insurance or technology companies. One key domain under this theme was the question of who should be held accountable in the case of adverse events arising from using AI.

CONCLUSIONS

While overall positivity persists in attitudes and preferences toward AI use in healthcare, some prevalent problems require more attention. There is a need to go beyond addressing algorithm-related issues to look at the translation of legislation and guidelines into practice to ensure fairness, accountability, transparency, and ethics in AI.

Insights on the Current State and Future Outlook of AI in Health Care: Expert Interview Study

BACKGROUND

Artificial intelligence (AI) is often promoted as a potential solution for many challenges health care systems face worldwide. However, its implementation in clinical practice lags behind its technological development.

OBJECTIVE

This study aims to gain insights into the current state and prospects of AI technology from the stakeholders most directly involved in its adoption in the health care sector whose perspectives have received limited attention in research to date.

METHODS

For this purpose, the perspectives of AI researchers and health care IT professionals in North America and Western Europe were collected and compared for profession-specific and regional differences. In this preregistered, mixed methods, cross-sectional study, 23 experts were interviewed using a semistructured guide. Data from the interviews were analyzed using deductive and inductive qualitative methods for the thematic analysis along with topic modeling to identify latent topics.

RESULTS

Through our thematic analysis, four major categories emerged: (1) the current state of AI systems in health care, (2) the criteria and requirements for implementing AI systems in health care, (3) the challenges in implementing AI systems in health care, and (4) the prospects of the technology. Experts discussed the capabilities and limitations of current AI systems in health care in addition to their prevalence and regional differences. Several criteria and requirements deemed necessary for the successful implementation of AI systems were identified, including the technology's performance and security, smooth system integration and human-AI interaction, costs, stakeholder involvement, and employee training. However, regulatory, logistical, and technical issues were identified as the most critical barriers to an effective technology implementation process. In the future, our experts predicted both various threats and many opportunities related to AI technology in the health care sector.

CONCLUSIONS

Our work provides new insights into the current state, criteria, challenges, and outlook for implementing AI technology in health care from the perspective of AI researchers and IT professionals in North America and Western Europe. For the full potential of AI-enabled technologies to be exploited and for them to contribute to solving current health care challenges, critical implementation criteria must be met, and all groups involved in the process must work together.

Exploring artificial intelligence in the Nigerian medical educational space: An online cross-sectional study of perceptions, risks and benefits among students and lecturers from ten universities

Background

The impact of artificial intelligence (AI) has been compared to that of the Internet and printing, evoking both apprehension and anticipation in an uncertain world.

Objective

This study aimed to explore the perceptions of medical students and faculty members from ten universities across Nigeria regarding AI.

Methods

Using Google Forms and WhatsApp, a cross-sectional online survey was administered to clinical year medical students and their lecturers from ten medical schools representing all the six geopolitical zones of Nigeria.

Results

The survey received 1003 responses, of which 708 (70.7%) were from students and 294 (29.3%) were from lecturers. Both groups displayed an average level of knowledge, with students (Median:4, range -5 to 12) significantly outperforming lecturers (Median:3, range -5 to 15). Social media (61.2%) was the most common form of first contact with AI. Participants demonstrated a favourable attitude towards AI, with a median score of 6.8 out of 10. Grammar checkers (62.3%) were the most commonly reported AI tool used, while ChatGPT (43.6%) was the most frequently mentioned dedicated AI tool. Students were significantly more likely than lecturers to have used AI tools in the past but <5% of both groups had received prior AI training. Excitement about the potential of AI slightly outweighed concerns regarding future risks. A significantly higher proportion of students compared to lecturers believed that AI could dehumanise health care (70.6% vs. 60.8%), render physicians redundant (57.6% vs. 34.7%), diminish physicians' skills (79.3% vs. 71.3%) and ultimately harm patients (28.6% vs. 20.6%).

Conclusion

The simultaneous fascination and apprehension with AI observed among both lecturers and students in our study mirrors the global trend. This finding was particularly evident in students who, despite possessing greater knowledge of AI compared to their lecturers, did not exhibit a corresponding reduction in their fear of AI.

Norwegian radiologists' expectations of artificial intelligence in mammographic screening - A cross-sectional survey

PURPOSE

To explore Norwegian breast radiologists' expectations of adding artificial intelligence (AI) in the interpretation procedure of screening mammograms.

METHODS

All breast radiologists involved in interpretation of screening mammograms in BreastScreen Norway during 2021 and 2022 (n = 98) were invited to take part in this anonymous cross-sectional survey about use of AI in mammographic screening. The questionnaire included background information of the respondents, their expectations, considerations of biases, and ethical and social implications of implementing AI in screen reading. Data was collected digitally and analyzed using descriptive statistics.

RESULTS

The response rate was 61% (60/98), and 67% (40/60) of the respondents were women. Sixty percent (36/60) reported ≥10 years' experience in screen reading, while 82% (49/60) reported no or limited experience with AI in health care. Eighty-two percent of the respondents were positive to explore AI in the interpretation procedure in mammographic screening. When used as decision support, 68% (41/60) expected AI to increase the radiologists' sensitivity for cancer detection. As potential challenges, 55% (33/60) reported lack of trust in the AI system and 45% (27/60) reported discrepancy between radiologists and AI systems as possible challenges. The risk of automation bias was considered high among 47% (28/60). Reduced time spent reading mammograms was rated as a potential benefit by 70% (42/60).

CONCLUSION

The radiologists reported positive expectations of AI in the interpretation procedure of screening mammograms. Efforts to minimize the risk of automation bias and increase trust in the AI systems are important before and during future implementation of the tool.

Centering Public Perceptions on Translating AI Into Clinical Practice: Patient and Public Involvement and Engagement Consultation Focus Group Study

BACKGROUND

Artificial intelligence (AI) is widely considered to be the new technical advancement capable of a large-scale modernization of health care. Considering AI's potential impact on the clinician-patient relationship, health care provision, and health care systems more widely, patients and the wider public should be a part of the development, implementation, and embedding of AI applications in health care. Failing to establish patient and public engagement and involvement (PPIE) can limit AI's impact.

OBJECTIVE

This study aims to (1) understand patients' and the public's perceived benefits and challenges for AI and (2) clarify how to best conduct PPIE in projects on translating AI into clinical practice, given public perceptions of AI.

METHODS

We conducted this qualitative PPIE focus-group consultation in the United Kingdom. A total of 17 public collaborators representing 7 National Institute of Health and Care Research Applied Research Collaborations across England participated in 1 of 3 web-based semistructured focus group discussions. We explored public collaborators' understandings, experiences, and perceptions of AI applications in health care. Transcripts were coanalyzed iteratively with 2 public coauthors using thematic analysis.

RESULTS

We identified 3 primary deductive themes with 7 corresponding inductive subthemes. Primary theme 1, advantages of implementing AI in health care, had 2 subthemes: system improvements and improve quality of patient care and shared decision-making. Primary theme 2, challenges of implementing AI in health care, had 3 subthemes: challenges with security, bias, and access; public misunderstanding of AI; and lack of human touch in care and decision-making. Primary theme 3, recommendations on PPIE for AI in health care, had 2 subthemes: experience, empowerment, and raising awareness; and acknowledging and supporting diversity in PPIE.

CONCLUSIONS

Patients and the public can bring unique perspectives on the development, implementation, and embedding of AI in health care. Early PPIE is therefore crucial not only to safeguard patients but also to increase the chances of acceptance of AI by the public and the impact AI can make in terms of outcomes.

Patients' Views on AI for Risk Prediction in Shared Decision-Making for Knee Replacement Surgery: Qualitative Interview Study

BACKGROUND

The use of artificial intelligence (AI) in decision-making around knee replacement surgery is increasing, and this technology holds promise to improve the prediction of patient outcomes. Ambiguity surrounds the definition of AI, and there are mixed views on its application in clinical settings.

OBJECTIVE

In this study, we aimed to explore the understanding and attitudes of patients who underwent knee replacement surgery regarding AI in the context of risk prediction for shared clinical decision-making.

METHODS

This qualitative study involved patients who underwent knee replacement surgery at a tertiary referral center for joint replacement surgery. The participants were selected based on their age and sex. Semistructured interviews explored the participants' understanding of AI and their opinions on its use in shared clinical decision-making. Data collection and reflexive thematic analyses were conducted concurrently. Recruitment continued until thematic saturation was achieved.

RESULTS

Thematic saturation was achieved with 19 interviews and confirmed with 1 additional interview, resulting in 20 participants being interviewed (female participants: n=11, 55%; male participants: n=9, 45%; median age: 66 years). A total of 11 (55%) participants had a substantial postoperative complication. Three themes captured the participants' understanding of AI and their perceptions of its use in shared clinical decision-making. The theme Expectations captured the participants' views of themselves as individuals with the right to self-determination as they sought therapeutic solutions tailored to their circumstances, needs, and desires, including whether to use AI at all. The theme Empowerment highlighted the potential of AI to enable patients to develop realistic expectations and equip them with personalized risk information to discuss in shared decision-making conversations with the surgeon. The theme Partnership captured the importance of symbiosis between AI and clinicians because AI has varied levels of interpretability and understanding of human emotions and empathy.

CONCLUSIONS

Patients who underwent knee replacement surgery in this study had varied levels of familiarity with AI and diverse conceptualizations of its definitions and capabilities. Educating patients about AI through nontechnical explanations and illustrative scenarios could help inform their decision to use it for risk prediction in the shared decision-making process with their surgeon. These findings could be used in the process of developing a questionnaire to ascertain the views of patients undergoing knee replacement surgery on the acceptability of AI in shared clinical decision-making. Future work could investigate the accuracy of this patient group's understanding of AI, beyond their familiarity with it, and how this influences their acceptance of its use. Surgeons may play a key role in finding a place for AI in the clinical setting as the uptake of this technology in health care continues to grow.

Evaluation of acceptance, attitude, and knowledge towards artificial intelligence and its application from the point of view of physicians and nurses: A provincial survey study in Iran: A cross-sectional descriptive-analytical study

Background and Aims

The prospect of using artificial intelligence (AI) in healthcare is bright and promising, and its use can have a significant impact on cost reduction and decrease the possibility of error and negligence among healthcare workers. This study aims to investigate the level of knowledge, attitude, and acceptance among Iranian physicians and nurses.

Methods

This cross-sectional descriptive-analytical study was conducted in eight public university hospitals located in Tehran on 400 physicians and nurses. To conduct the study, convenient sampling was used with the help of researcher-made questionnaires. Statistical analysis was done by SPSS 21 The mean and standard deviation and Chi-square and Fisher's exact tests were used.

Results

In this study, the level of knowledge among the research subjects was average (14.66 ± 4.53), the level of their attitude toward AI was relatively favorable (47.81 ± 6.74), and their level of acceptance of AI was average (103.19 ± 13.70). Moreover, from the participant's perspective, AI in medicine is most widely used in increasing the accuracy of diagnostic tests (86.5%), identifying drug interactions (82.75%), and helping to analyze medical tests and imaging (80%). There was a statistically significant relationship between the variable of acceptance of AI and the participant's level of education (p = 0.028), participation in an AI training course (p = 0.022), and the hospital department where they worked (p < 0.001).

Conclusion

In this study, both the knowledge and the acceptance of the participants towards AI were proved to be at an average level and the attitude towards AI was relatively favorable, which is in contrast with the very rapid and inevitable expansion of AI. Although our participants were aware of the growing use of AI in medicine, they had a cautious attitude toward this.

An example of governance for AI in health services from Aotearoa New Zealand

Artificial Intelligence (AI) is undergoing rapid development, meaning that potential risks in application are not able to be fully understood. Multiple international principles and guidance documents have been published to guide the implementation of AI tools in various industries, including healthcare practice. In Aotearoa New Zealand (NZ) we recognised that the challenge went beyond simply adapting existing risk frameworks and governance guidance to our specific health service context and population. We also deemed prioritising the voice of Māori (the indigenous people of Aotearoa NZ) a necessary aspect of honouring Te Tiriti (the Treaty of Waitangi), as well as prioritising the needs of healthcare service users and their families. Here we report on the development and establishment of comprehensive and effective governance over the development and implementation of AI tools within a health service in Aotearoa NZ. The implementation of the framework in practice includes testing with real-world proposals and ongoing iteration and refinement of our processes.

The intersection of video capsule endoscopy and artificial intelligence: addressing unique challenges using machine learning

Introduction

Technical burdens and time-intensive review processes limit the practical utility of video capsule endoscopy (VCE). Artificial intelligence (AI) is poised to address these limitations, but the intersection of AI and VCE reveals challenges that must first be overcome. We identified five challenges to address. Challenge #1: VCE data are stochastic and contains significant artifact. Challenge #2: VCE interpretation is cost-intensive. Challenge #3: VCE data are inherently imbalanced. Challenge #4: Existing VCE AIMLT are computationally cumbersome. Challenge #5: Clinicians are hesitant to accept AIMLT that cannot explain their process.

Methods

An anatomic landmark detection model was used to test the application of convolutional neural networks (CNNs) to the task of classifying VCE data. We also created a tool that assists in expert annotation of VCE data. We then created more elaborate models using different approaches including a multi-frame approach, a CNN based on graph representation, and a few-shot approach based on meta-learning.

Results

When used on full-length VCE footage, CNNs accurately identified anatomic landmarks (99.1%), with gradient weighted-class activation mapping showing the parts of each frame that the CNN used to make its decision. The graph CNN with weakly supervised learning (accuracy 89.9%, sensitivity of 91.1%), the few-shot model (accuracy 90.8%, precision 91.4%, sensitivity 90.9%), and the multi-frame model (accuracy 97.5%, precision 91.5%, sensitivity 94.8%) performed well.

Discussion

Each of these five challenges is addressed, in part, by one of our AI-based models. Our goal of producing high performance using lightweight models that aim to improve clinician confidence was achieved.

Development and validation of the AI attitude scale (AIAS-4): a brief measure of general attitude toward artificial intelligence

The rapid advancement of artificial intelligence (AI) has generated an increasing demand for tools that can assess public attitudes toward AI. This study proposes the development and the validation of the AI Attitude Scale (AIAS), a concise self-report instrument designed to evaluate public perceptions of AI technology. The first version of the AIAS that the present manuscript proposes comprises five items, including one reverse-scored item, which aims to gauge individuals' beliefs about AI's influence on their lives, careers, and humanity overall. The scale is designed to capture attitudes toward AI, focusing on the perceived utility and potential impact of technology on society and humanity. The psychometric properties of the scale were investigated using diverse samples in two separate studies. An exploratory factor analysis was initially conducted on a preliminary 5-item version of the scale. Such exploratory validation study revealed the need to divide the scale into two factors. While the results demonstrated satisfactory internal consistency for the overall scale and its correlation with related psychometric measures, separate analyses for each factor showed robust internal consistency for Factor 1 but insufficient internal consistency for Factor 2. As a result, a second version of the scale is developed and validated, omitting the item that displayed weak correlation with the remaining items in the questionnaire. The refined final 1-factor, 4-item AIAS demonstrated superior overall internal consistency compared to the initial 5-item scale and the proposed factors. Further confirmatory factor analyses, performed on a different sample of participants, confirmed that the 1-factor model (4-items) of the AIAS exhibited an adequate fit to the data, providing additional evidence for the scale's structural validity and generalizability across diverse populations. In conclusion, the analyses reported in this article suggest that the developed and validated 4-items AIAS can be a valuable instrument for researchers and professionals working on AI development who seek to understand and study users' general attitudes toward AI.

Innovation in healthcare: leadership perceptions about the innovation characteristics of artificial intelligence-a qualitative interview study with healthcare leaders in Sweden

BACKGROUND

Despite the extensive hopes and expectations for value creation resulting from the implementation of artificial intelligence (AI) applications in healthcare, research has predominantly been technology-centric rather than focused on the many changes that are required in clinical practice for the technology to be successfully implemented. The importance of leaders in the successful implementation of innovations in healthcare is well recognised, yet their perspectives on the specific innovation characteristics of AI are still unknown. The aim of this study was therefore to explore the perceptions of leaders in healthcare concerning the innovation characteristics of AI intended to be implemented into their organisation.

METHODS

The study had a deductive qualitative design, using constructs from the innovation domain in the Consolidated Framework for Implementation Research (CFIR). Interviews were conducted with 26 leaders in healthcare.

RESULTS

Participants perceived that AI could provide relative advantages when it came to care management, supporting clinical decisions, and the early detection of disease and risk of disease. The development of AI in the organisation itself was perceived as the main current innovation source. The evidence base behind AI technology was questioned, in relation to its transparency, potential quality improvement, and safety risks. Although the participants acknowledged AI to be superior to human action in terms of effectiveness and precision in some situations, they also expressed uncertainty about the adaptability and trialability of AI. Complexities such as the characteristics of the technology, the lack of conceptual consensus about AI, and the need for a variety of implementation strategies to accomplish transformative change in practice were identified, as were uncertainties about the costs involved in AI implementation.

CONCLUSION

Healthcare leaders not only saw potential in the technology and its use in practice, but also felt that AI's opacity limits its evidence strength and that complexities in relation to AI itself and its implementation influence its current use in healthcare practice. More research is needed based on actual experiences using AI applications in real-world situations and their impact on clinical practice. New theories, models, and frameworks may need to be developed to meet challenges related to the implementation of AI in healthcare.

Democratising or disrupting diagnosis? Ethical issues raised by the use of AI tools for rare disease diagnosis

Computational phenotyping (CP) technology uses facial recognition algorithms to classify and potentially diagnose rare genetic disorders on the basis of digitised facial images. This AI technology has a number of research as well as clinical applications, such as supporting diagnostic decision-making. Using the example of CP, we examine stakeholders' views of the benefits and costs of using AI as a diagnostic tool within the clinic. Through a series of in-depth interviews (n ​= ​20) with: clinicians, clinical researchers, data scientists, industry and support group representatives, we report stakeholder views regarding the adoption of this technology in a clinical setting. While most interviewees were supportive of employing CP as a diagnostic tool in some capacity we observed ambivalence around the potential for artificial intelligence to overcome diagnostic uncertainty in a clinical context. Thus, while there was widespread agreement amongst interviewees concerning the public benefits of AI assisted diagnosis, namely, its potential to increase diagnostic yield and enable faster more objective and accurate diagnoses by up skilling non specialists and thereby enabling access to diagnosis that is potentially lacking, interviewees also raised concerns about ensuring algorithmic reliability, expunging algorithmic bias and that the use of AI could result in deskilling the specialist clinical workforce. We conclude that, prior to widespread clinical implementation, on-going reflection is needed regarding the trade-offs required to determine acceptable levels of bias and conclude that diagnostic AI tools should only be employed as an assistive technology within the dysmorphology clinic.

An Assessment of How Clinicians and Staff Members Use a Diabetes Artificial Intelligence Prediction Tool: Mixed Methods Study

BACKGROUND

Nearly one-third of patients with diabetes are poorly controlled (hemoglobin A1c≥9%). Identifying at-risk individuals and providing them with effective treatment is an important strategy for preventing poor control.

OBJECTIVE

This study aims to assess how clinicians and staff members would use a clinical decision support tool based on artificial intelligence (AI) and identify factors that affect adoption.

METHODS

This was a mixed methods study that combined semistructured interviews and surveys to assess the perceived usefulness and ease of use, intent to use, and factors affecting tool adoption. We recruited clinicians and staff members from practices that manage diabetes. During the interviews, participants reviewed a sample electronic health record alert and were informed that the tool uses AI to identify those at high risk for poor control. Participants discussed how they would use the tool, whether it would contribute to care, and the factors affecting its implementation. In a survey, participants reported their demographics; rank-ordered factors influencing the adoption of the tool; and reported their perception of the tool's usefulness as well as their intent to use, ease of use, and organizational support for use. Qualitative data were analyzed using a thematic content analysis approach. We used descriptive statistics to report demographics and analyze the findings of the survey.

RESULTS

In total, 22 individuals participated in the study. Two-thirds (14/22, 63%) of respondents were physicians. Overall, 36% (8/22) of respondents worked in academic health centers, whereas 27% (6/22) of respondents worked in federally qualified health centers. The interviews identified several themes: this tool has the potential to be useful because it provides information that is not currently available and can make care more efficient and effective; clinicians and staff members were concerned about how the tool affects patient-oriented outcomes and clinical workflows; adoption of the tool is dependent on its validation, transparency, actionability, and design and could be increased with changes to the interface and usability; and implementation would require buy-in and need to be tailored to the demands and resources of clinics and communities. Survey findings supported these themes, as 77% (17/22) of participants somewhat, moderately, or strongly agreed that they would use the tool, whereas these figures were 82% (18/22) for usefulness, 82% (18/22) for ease of use, and 68% (15/22) for clinic support. The 2 highest ranked factors affecting adoption were whether the tool improves health and the accuracy of the tool.

CONCLUSIONS

Most participants found the tool to be easy to use and useful, although they had concerns about alert fatigue, bias, and transparency. These data will be used to enhance the design of an AI tool.

Artificial Intelligence and Groups: Effects of Attitudes and Discretion on Collaboration

We theorize about human-team collaboration with AI by drawing upon research in groups and teams, social psychology, information systems, engineering, and beyond. Based on our review, we focus on two main issues in the teams and AI arena. The first is whether the team generally views AI positively or negatively. The second is whether the decision to use AI is left up to the team members (voluntary use of AI) or mandated by top management or other policy-setters in the organization. These two aspects guide our creation of a team-level conceptual framework modeling how AI introduced as a mandated addition to the team can have asymmetric effects on collaboration level depending on the team’s attitudes about AI. When AI is viewed positively by the team, the effect of mandatory use suppresses collaboration in the team. But when a team has negative attitudes toward AI, mandatory use elevates team collaboration. Our model emphasizes the need for managing team attitudes and discretion strategies and promoting new research directions regarding AI’s implications for teamwork.

The Use of Artificial Intelligence in Clinical Care: A Values-Based Guide for Shared Decision Making

Clinical applications of artificial intelligence (AI) in healthcare, including in the field of oncology, have the potential to advance diagnosis and treatment. The literature suggests that patient values should be considered in decision making when using AI in clinical care; however, there is a lack of practical guidance for clinicians on how to approach these conversations and incorporate patient values into clinical decision making. We provide a practical, values-based guide for clinicians to assist in critical reflection and the incorporation of patient values into shared decision making when deciding to use AI in clinical care. Values that are relevant to patients, identified in the literature, include trust, privacy and confidentiality, non-maleficence, safety, accountability, beneficence, autonomy, transparency, compassion, equity, justice, and fairness. The guide offers questions for clinicians to consider when adopting the potential use of AI in their practice; explores illness understanding between the patient and clinician; encourages open dialogue of patient values; reviews all clinically appropriate options; and makes a shared decision of what option best meets the patient's values. The guide can be used for diverse clinical applications of AI.

Neurology education in the era of artificial intelligence

PURPOSE OF REVIEW

The practice of neurology is undergoing a paradigm shift because of advances in the field of data science, artificial intelligence, and machine learning. To ensure a smooth transition, physicians must have the knowledge and competence to apply these technologies in clinical practice. In this review, we describe physician perception and preparedness, as well as current state for clinical applications of artificial intelligence and machine learning in neurology.

RECENT FINDINGS

Digital health including artificial intelligence-based/machine learning-based technology has made significant inroads into various aspects of healthcare including neurological care. Surveys of physicians and healthcare stakeholders suggests an overall positive perception about the benefits of artificial intelligence/machine learning in clinical practice. This positive perception is tempered by concerns for lack of knowledge and limited opportunities to build competence in artificial intelligence/machine learning technology. Literature about neurologist's perception and preparedness towards artificial intelligence/machine learning-based technology is scant. There are very few opportunities for physicians particularly neurologists to learn about artificial intelligence/machine learning-based technology.

SUMMARY

Neurologists have not been surveyed about their perception and preparedness to adopt artificial intelligence/machine learning-based technology in clinical practice. We propose development of a practical artificial intelligence/machine learning curriculum to enhance neurologists' competence in these newer technologies.

Artificial intelligence-based clinical decision support in pediatrics

Machine learning models may be integrated into clinical decision support (CDS) systems to identify children at risk of specific diagnoses or clinical deterioration to provide evidence-based recommendations. This use of artificial intelligence models in clinical decision support (AI-CDS) may have several advantages over traditional "rule-based" CDS models in pediatric care through increased model accuracy, with fewer false alerts and missed patients. AI-CDS tools must be appropriately developed, provide insight into the rationale behind decisions, be seamlessly integrated into care pathways, be intuitive to use, answer clinically relevant questions, respect the content expertise of the healthcare provider, and be scientifically sound. While numerous machine learning models have been reported in pediatric care, their integration into AI-CDS remains incompletely realized to date. Important challenges in the application of AI models in pediatric care include the relatively lower rates of clinically significant outcomes compared to adults, and the lack of sufficiently large datasets available necessary for the development of machine learning models. In this review article, we summarize key concepts related to AI-CDS, its current application to pediatric care, and its potential benefits and risks. IMPACT: The performance of clinical decision support may be enhanced by the utilization of machine learning-based algorithms to improve the predictive performance of underlying models. Artificial intelligence-based clinical decision support (AI-CDS) uses models that are experientially improved through training and are particularly well suited toward high-dimensional data. The application of AI-CDS toward pediatric care remains limited currently but represents an important area of future research.

Involving multiple stakeholders in assessing and reviewing a novel data visualisation tool for a national neonatal data asset

OBJECTIVES

We involved public and professional stakeholders to assess a novel data interrogation tool, the Neonatal Health Intelligence Tool, for a National Data Asset, the National Neonatal Research Database.

METHODS

We recruited parents, preterm adults, data managers, clinicians, network managers and researchers (trialists and epidemiologists) for consultations demonstrating a prototype tool and semi-structured discussion. A thematic analysis of consultations is reported by stakeholder group.

RESULTS

We held nine on-line consultations (March-December 2021), with 24 stakeholders: parents (n=8), preterm adults (n=2), data managers (n=3), clinicians (n=3), network managers (n=2), triallists (n=3) and epidemiologists (n=3). We identified four themes from parents/preterm adults: struggling to consume information, Dads and data, bring data to life and yearning for predictions; five themes from data managers/clinicians/network managers: benchmarking, clinical outcomes, transfers and activity, the impact of socioeconomic background and ethnicity, and timeliness of updates and widening availability; and one theme from researchers: interrogating the data.

DISCUSSION

Other patient and public involvement (PPI) studies have reported that data tools generate concerns; our stakeholders had none. They were unanimously supportive and enthusiastic, citing visualisation as the tool's greatest strength. Stakeholders had no criticisms; instead, they recognised the tool's potential and wanted more features. Parents saw the tool as an opportunity to inform themselves without burdening clinicians, while clinicians welcomed an aid to explaining potential outcomes to parents.

CONCLUSION

All stakeholder groups recognised the need for the tool, praising its content and format. PPI consultations with all key groups, and their synthesis, illustrated desire for additional uses from it.

How, for whom, and in what contexts will artificial intelligence be adopted in pathology? A realist interview study

OBJECTIVE

There is increasing interest in using artificial intelligence (AI) in pathology to improve accuracy and efficiency. Studies of clinicians' perceptions of AI have found only moderate acceptability, suggesting further research is needed regarding integration into clinical practice. This study aimed to explore stakeholders' theories concerning how and in what contexts AI is likely to become integrated into pathology.

MATERIALS AND METHODS

A literature review provided tentative theories that were revised through a realist interview study with 20 pathologists and 5 pathology trainees. Questions sought to elicit whether, and in what ways, the tentative theories fitted with interviewees' perceptions and experiences. Analysis focused on identifying the contextual factors that may support or constrain uptake of AI in pathology.

RESULTS

Interviews highlighted the importance of trust in AI, with interviewees emphasizing evaluation and the opportunity for pathologists to become familiar with AI as means for establishing trust. Interviewees expressed a desire to be involved in design and implementation of AI tools, to ensure such tools address pressing needs, but needs vary by subspecialty. Workflow integration is desired but whether AI tools should work automatically will vary according to the task and the context.

CONCLUSIONS

It must not be assumed that AI tools that provide benefit in one subspecialty will provide benefit in others. Pathologists should be involved in the decision to introduce AI, with opportunity to assess strengths and weaknesses. Further research is needed concerning the evidence required to satisfy pathologists regarding the benefits of AI.

Ethical Implications of Artificial Intelligence in Population Health and the Public’s Role in its Governance: Perspectives from a Citizen and Expert Panel (Preprint)

BACKGROUND

Artificial intelligence (AI) systems are widely used in the health care sector. Mainly applied for individualized care, AI is increasingly aimed at population health. This raises important ethical considerations but also calls for responsible governance, considering that this will affect the population. However, the literature points to a lack of citizen participation in the governance of AI in health. Therefore, it is necessary to investigate the governance of the ethical and societal implications of AI in population health.

OBJECTIVE

This study aimed to explore the perspectives and attitudes of citizens and experts regarding the ethics of AI in population health, the engagement of citizens in AI governance, and the potential of a digital app to foster citizen engagement.

METHODS

We recruited a panel of 21 citizens and experts. Using a web-based survey, we explored their perspectives and attitudes on the ethical issues of AI in population health, the relative role of citizens and other actors in AI governance, and the ways in which citizens can be supported to participate in AI governance through a digital app. The responses of the participants were analyzed quantitatively and qualitatively.

RESULTS

According to the participants, AI is perceived to be already present in population health and its benefits are regarded positively, but there is a consensus that AI has substantial societal implications. The participants also showed a high level of agreement toward involving citizens into AI governance. They highlighted the aspects to be considered in the creation of a digital app to foster this involvement. They recognized the importance of creating an app that is both accessible and transparent.

CONCLUSIONS

These results offer avenues for the development of a digital app to raise awareness, to survey, and to support citizens' decision-making regarding the ethical, legal, and social issues of AI in population health.

Predicting attitudes towards screening for neurodegenerative diseases using OCT and artificial intelligence: Findings from a literature review

Recent developments in artificial intelligence (AI) and machine learning raise the possibility of screening and early diagnosis for neurodegenerative diseases, using 3D scans of the retina. The eventual value of such screening will depend not only on scientific metrics such as specificity and sensitivity but, critically, also on public attitudes and uptake. Differential screening rates for various screening programmes in England indicate that multiple factors influence uptake. In this narrative literature review, some of these potential factors are explored in relation to predicting uptake of an early screening tool for neurodegenerative diseases using AI. These include: awareness of the disease, perceived risk, social influence, the use of AI, previous screening experience, socioeconomic status, health literacy, uncontrollable mortality risk, and demographic factors. The review finds the strongest and most consistent predictors to be ethnicity, social influence, the use of AI, and previous screening experience. Furthermore, it is likely that factors also interact to predict the uptake of such a tool. However, further experimental work is needed both to validate these predictions and explore interactions between the significant predictors.

Acceptance of clinical artificial intelligence among physicians and medical students: A systematic review with cross-sectional survey

Background

Artificial intelligence (AI) needs to be accepted and understood by physicians and medical students, but few have systematically assessed their attitudes. We investigated clinical AI acceptance among physicians and medical students around the world to provide implementation guidance.

Materials and methods

We conducted a two-stage study, involving a foundational systematic review of physician and medical student acceptance of clinical AI. This enabled us to design a suitable web-based questionnaire which was then distributed among practitioners and trainees around the world.

Results

Sixty studies were included in this systematic review, and 758 respondents from 39 countries completed the online questionnaire. Five (62.50%) of eight studies reported 65% or higher awareness regarding the application of clinical AI. Although, only 10–30% had actually used AI and 26 (74.28%) of 35 studies suggested there was a lack of AI knowledge. Our questionnaire uncovered 38% awareness rate and 20% utility rate of clinical AI, although 53% lacked basic knowledge of clinical AI. Forty-five studies mentioned attitudes toward clinical AI, and over 60% from 38 (84.44%) studies were positive about AI, although they were also concerned about the potential for unpredictable, incorrect results. Seventy-seven percent were optimistic about the prospect of clinical AI. The support rate for the statement that AI could replace physicians ranged from 6 to 78% across 40 studies which mentioned this topic. Five studies recommended that efforts should be made to increase collaboration. Our questionnaire showed 68% disagreed that AI would become a surrogate physician, but believed it should assist in clinical decision-making. Participants with different identities, experience and from different countries hold similar but subtly different attitudes.

Conclusion

Most physicians and medical students appear aware of the increasing application of clinical AI, but lack practical experience and related knowledge. Overall, participants have positive but reserved attitudes about AI. In spite of the mixed opinions around clinical AI becoming a surrogate physician, there was a consensus that collaborations between the two should be strengthened. Further education should be conducted to alleviate anxieties associated with change and adopting new technologies.

A qualitative study to explore opinions of Saudi Arabian radiologists concerning AI-based applications and their impact on the future of the radiology

Objective

The aim of this study was to explore opinions and views towards radiology AI among Saudi Arabian radiologists including both consultants and trainees.

Methods

A qualitative approach was adopted, with radiologists working in radiology departments in the Western region of Saudi Arabia invited to participate in this interview-based study. Semi-structured interviews (n = 30) were conducted with consultant radiologists and trainees. A qualitative data analysis framework was used based on Miles and Huberman's philosophical underpinnings.

Results

Several factors, such as lack of training and support, were attributed to the non-use of AI-based applications in clinical practice and the absence of radiologists' involvement in AI development. Despite the expected benefits and positive impacts of AI on radiology, a reluctance to use AI-based applications might exist due to a lack of knowledge, fear of error and concerns about losing jobs and/or power. Medical students' radiology education and training appeared to be influenced by the absence of a governing body and training programmes.

Conclusion

The results of this study support the establishment of a governing body or national association to work in parallel with universities in monitoring training and integrating AI into the medical education curriculum and residency programmes.

Advances in knowledge

An extensive debate about AI-based applications and their potential effects was noted, and considerable exceptions of transformative impact may occur when AI is fully integrated into clinical practice. Therefore, future education and training programmes on how to work with AI-based applications in clinical practice may be recommended.

Attitudes and perception of artificial intelligence in healthcare: A cross-sectional survey among patients

Objective

The attitudes about the usage of artificial intelligence in healthcare are controversial. Unlike the perception of healthcare professionals, the attitudes of patients and their companions have been of less interest so far. In this study, we aimed to investigate the perception of artificial intelligence in healthcare among this highly relevant group along with the influence of digital affinity and sociodemographic factors.

Methods

We conducted a cross-sectional study using a paper-based questionnaire with patients and their companions at a German tertiary referral hospital from December 2019 to February 2020. The questionnaire consisted of three sections examining (a) the respondents’ technical affinity, (b) their perception of different aspects of artificial intelligence in healthcare and (c) sociodemographic characteristics.

Results

From a total of 452 participants, more than 90% already read or heard about artificial intelligence, but only 24% reported good or expert knowledge. Asked on their general perception, 53.18% of the respondents rated the use of artificial intelligence in medicine as positive or very positive, but only 4.77% negative or very negative. The respondents denied concerns about artificial intelligence, but strongly agreed that artificial intelligence must be controlled by a physician. Older patients, women, persons with lower education and technical affinity were more cautious on the healthcare-related artificial intelligence usage.

Conclusions

German patients and their companions are open towards the usage of artificial intelligence in healthcare. Although showing only a mediocre knowledge about artificial intelligence, a majority rated artificial intelligence in healthcare as positive. Particularly, patients insist that a physician supervises the artificial intelligence and keeps ultimate responsibility for diagnosis and therapy.

Expectations for Artificial Intelligence (AI) in Psychiatry

PURPOSE OF REVIEW

Artificial intelligence (AI) is often presented as a transformative technology for clinical medicine even though the current technology maturity of AI is low. The purpose of this narrative review is to describe the complex reasons for the low technology maturity and set realistic expectations for the safe, routine use of AI in clinical medicine.

RECENT FINDINGS

For AI to be productive in clinical medicine, many diverse factors that contribute to the low maturity level need to be addressed. These include technical problems such as data quality, dataset shift, black-box opacity, validation and regulatory challenges, and human factors such as a lack of education in AI, workflow changes, automation bias, and deskilling. There will also be new and unanticipated safety risks with the introduction of AI. The solutions to these issues are complex and will take time to discover, develop, validate, and implement. However, addressing the many problems in a methodical manner will expedite the safe and beneficial use of AI to augment medical decision making in psychiatry.