Artificial intelligence and the ongoing need for empathy, compassion and trust in healthcare

Use and Application of Large Language Models for Patient Questions Following Total Knee Arthroplasty

BACKGROUND

A consumer-focused health care model not only allows unprecedented access to information, but equally warrants consideration of the appropriateness of providing accurate patient health information. Nurses play a large role in influencing patient satisfaction following total knee arthroplasty (TKA), but they come at a cost. A specific natural language artificial intelligence (AI) model, ChatGPT (Chat Generative Pre-trained Transformer), has accumulated over 100 million users within months of launching. As such, we aimed to compare: (1) orthopaedic surgeons' evaluation of the appropriateness of the answers to the most frequently asked patient questions after TKA; and (2) patients' comfort level in answering their postoperative questions by using answers provided by arthroplasty-trained nurses and ChatGPT.

METHODS

We prospectively created 60 questions based on the most commonly asked patient questions following TKA. There were 3 fellowship-trained surgeons who assessed the answers provided by arthroplasty-trained nurses and ChatGPT-4 to each of the questions. The surgeons graded each set of responses based on clinical judgment as: (1) "appropriate," (2) "inappropriate" if the response contained inappropriate information, or (3) "unreliable," if the responses provided inconsistent content. Patients' comfort level and trust in AI were assessed using Research Electronic Data Capture (REDCap) hosted at our local hospital.

RESULTS

The surgeons graded 44 out of 60 (73.3%) responses for the arthroplasty-trained nurses and 44 out of 60 (73.3%) for ChatGPT to be "appropriate." There were 4 responses graded "inappropriate" and one response graded "unreliable" provided by the nurses. For the ChatGPT response, there were 5 responses graded "inappropriate" and no responses graded "unreliable." There were 136 patients (53.8%) who were more comfortable with the answers provided by ChatGPT compared to 86 patients (34.0%) who preferred the answers from arthroplasty-trained nurses. Of the 253 patients, 233 (92.1%) were uncertain if they would trust AI to answer their postoperative questions. There were 127 patients (50.2%) who answered that if they knew the previous answer was provided by ChatGPT, their comfort level in trusting the answer would change.

CONCLUSIONS

One potential use of ChatGPT can be found in providing appropriate answers to patient questions after TKA. At our institution, cost expenditures can potentially be minimized while maintaining patient satisfaction. Inevitably, successful implementation is dependent on the ability to provide information that is credible and in accordance with the objectives of both physicians and patients.

LEVEL OF EVIDENCE

III.

Biomedical Data Science, Artificial Intelligence, and Ethics: Navigating Challenges in the Face of Explosive Growth

Advances in biomedical data science and artificial intelligence (AI) are profoundly changing the landscape of healthcare. This article reviews the ethical issues that arise with the development of AI technologies, including threats to privacy, data security, consent, and justice, as they relate to donors of tissue and data. It also considers broader societal obligations, including the importance of assessing the unintended consequences of AI research in biomedicine. In addition, this article highlights the challenge of rapid AI development against the backdrop of disparate regulatory frameworks, calling for a global approach to address concerns around data misuse, unintended surveillance, and the equitable distribution of AI's benefits and burdens. Finally, a number of potential solutions to these ethical quandaries are offered. Namely, the merits of advocating for a collaborative, informed, and flexible regulatory approach that balances innovation with individual rights and public welfare, fostering a trustworthy AI-driven healthcare ecosystem, are discussed.

Development and evaluation of a prediction model for peripheral artery disease-related major adverse limb events using novel biomarker data

OBJECTIVE

Prognostic tools for individuals with peripheral artery disease (PAD) remain limited. We developed prediction models for 3-year PAD-related major adverse limb events (MALE) using demographic, clinical, and biomarker data previously validated by our group.

METHODS

We performed a prognostic study using a prospectively recruited cohort of patients with PAD (n = 569). Demographic/clinical data were recorded including sex, age, comorbidities, previous procedures, and medications. Plasma concentrations of three biomarkers (N-terminal pro-B-type natriuretic peptide [NT-proBNP], fatty acid binding protein 3 [FABP3], and FABP4) were measured at baseline. The cohort was followed for 3 years. MALE was the primary outcome (composite of open/endovascular vascular intervention or major amputation). We trained three machine learning models with 10-fold cross-validation using demographic, clinical, and biomarker data (random forest, decision trees, and Extreme Gradient Boosting [XGBoost]) to predict 3-year MALE in patients. Area under the receiver operating characteristic curve (AUROC) was the primary model evaluation metric.

RESULTS

Three-year MALE was observed in 162 patients (29%). XGBoost was the top-performing predictive model for 3-year MALE, achieving the following performance metrics: AUROC = 0.88 (95% confidence interval [CI], 0.84-0.94); sensitivity, 88%; specificity, 84%; positive predictive value, 83%; and negative predictive value, 91% on test set data. On an independent validation cohort of patients with PAD, XGBoost attained an AUROC of 0.87 (95% CI, 0.82-0.90). The 10 most important predictors of 3-year MALE consisted of: (1) FABP3; (2) FABP4; (3) age; (4) NT-proBNP; (5) active smoking; (6) diabetes; (7) hypertension; (8) dyslipidemia; (9) coronary artery disease; and (10) sex.

CONCLUSIONS

We built robust machine learning algorithms that accurately predict 3-year MALE in patients with PAD using demographic, clinical, and novel biomarker data. Our algorithms can support risk stratification of patients with PAD for additional vascular evaluation and early aggressive medical management, thereby improving outcomes. Further validation of our models for clinical implementation is warranted.

AI in Radiology: Navigating Medical Responsibility

The application of Artificial Intelligence (AI) facilitates medical activities by automating routine tasks for healthcare professionals. AI augments but does not replace human decision-making, thus complicating the process of addressing legal responsibility. This study investigates the legal challenges associated with the medical use of AI in radiology, analyzing relevant case law and literature, with a specific focus on professional liability attribution. In the case of an error, the primary responsibility remains with the physician, with possible shared liability with developers according to the framework of medical device liability. If there is disagreement with the AI's findings, the physician must not only pursue but also justify their choices according to prevailing professional standards. Regulations must balance the autonomy of AI systems with the need for responsible clinical practice. Effective use of AI-generated evaluations requires knowledge of data dynamics and metrics like sensitivity and specificity, even without a clear understanding of the underlying algorithms: the opacity (referred to as the "black box phenomenon") of certain systems raises concerns about the interpretation and actual usability of results for both physicians and patients. AI is redefining healthcare, underscoring the imperative for robust liability frameworks, meticulous updates of systems, and transparent patient communication regarding AI involvement.

Patients' and Clinicians' Perceptions of the Clinical Utility of Predictive Risk Models for Chemotherapy-Related Symptom Management: Qualitative Exploration Using Focus Groups and Interviews

BACKGROUND

Interest in the application of predictive risk models (PRMs) in health care to identify people most likely to experience disease and treatment-related complications is increasing. In cancer care, these techniques are focused primarily on the prediction of survival or life-threatening toxicities (eg, febrile neutropenia). Fewer studies focus on the use of PRMs for symptoms or supportive care needs. The application of PRMs to chemotherapy-related symptoms (CRS) would enable earlier identification and initiation of prompt, personalized, and tailored interventions. While some PRMs exist for CRS, few were translated into clinical practice, and human factors associated with their use were not reported.

OBJECTIVE

We aim to explore patients' and clinicians' perspectives of the utility and real-world application of PRMs to improve the management of CRS.

METHODS

Focus groups (N=10) and interviews (N=5) were conducted with patients (N=28) and clinicians (N=26) across 5 European countries. Interactions were audio-recorded, transcribed verbatim, and analyzed thematically.

RESULTS

Both clinicians and patients recognized the value of having individualized risk predictions for CRS and appreciated how this type of information would facilitate the provision of tailored preventative treatments or supportive care interactions. However, cautious and skeptical attitudes toward the use of PRMs in clinical care were noted by both groups, particularly in relationship to the uncertainty regarding how the information would be generated. Visualization and presentation of PRM information in a usable and useful format for both patients and clinicians was identified as a challenge to their successful implementation in clinical care.

CONCLUSIONS

Findings from this study provide information on clinicians' and patients' perspectives on the clinical use of PRMs for the management of CRS. These international perspectives are important because they provide insight into the risks and benefits of using PRMs to evaluate CRS. In addition, they highlight the need to find ways to more effectively present and use this information in clinical practice. Further research that explores the best ways to incorporate this type of information while maintaining the human side of care is warranted.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02356081; https://clinicaltrials.gov/study/NCT02356081.

Revolutionizing diagnostic pathology: The emergence and impact of artificial intelligence-what doesn't kill you makes you stronger?

This study explored the integration and impact of artificial intelligence (AI) in diagnostic pathology, particularly dermatopathology, assessing its challenges and potential solutions for global health care enhancement. A comprehensive literature search in PubMed and Google Scholar, conducted on March 30, 2023, and using terms related to AI, pathology, and machine learning, yielded 44 relevant publications. These were analyzed under themes including the evolution of deep learning in pathology, AI's role in replacing pathologists, development challenges of diagnostic algorithms, clinical implementation hurdles, strategies for practical application in dermatopathology, and future prospects of AI in this field. The findings highlight AI's transformative potential in pathology, underscore the need for ongoing research, collaboration, and regulatory dialogue, and emphasize the importance of addressing the ethical and practical challenges in AI implementation for improved global health care outcomes.

Perceptions of Family Physicians About Applying AI in Primary Health Care: Case Study From a Premier Health Care Organization

BACKGROUND

The COVID-19 pandemic has led to the rapid proliferation of artificial intelligence (AI), which was not previously anticipated; this is an unforeseen development. The use of AI in health care settings is increasing, as it proves to be a promising tool for transforming health care systems, improving operational and business processes, and efficiently simplifying health care tasks for family physicians and health care administrators. Therefore, it is necessary to assess the perspective of family physicians on AI and its impact on their job roles.

OBJECTIVE

This study aims to determine the impact of AI on the management and practices of Qatar's Primary Health Care Corporation (PHCC) in improving health care tasks and service delivery. Furthermore, it seeks to evaluate the impact of AI on family physicians' job roles, including associated risks and ethical ramifications from their perspective.

METHODS

We conducted a cross-sectional survey and sent a web-based questionnaire survey link to 724 practicing family physicians at the PHCC. In total, we received 102 eligible responses.

RESULTS

Of the 102 respondents, 72 (70.6%) were men and 94 (92.2%) were aged between 35 and 54 years. In addition, 58 (56.9%) of the 102 respondents were consultants. The overall awareness of AI was 80 (78.4%) out of 102, with no difference between gender (P=.06) and age groups (P=.12). AI is perceived to play a positive role in improving health care practices at PHCC (P<.001), managing health care tasks (P<.001), and positively impacting health care service delivery (P<.001). Family physicians also perceived that their clinical, administrative, and opportunistic health care management roles were positively influenced by AI (P<.001). Furthermore, perceptions of family physicians indicate that AI improves operational and human resource management (P<.001), does not undermine patient-physician relationships (P<.001), and is not considered superior to human physicians in the clinical judgment process (P<.001). However, its inclusion is believed to decrease patient satisfaction (P<.001). AI decision-making and accountability were recognized as ethical risks, along with data protection and confidentiality. The optimism regarding using AI for future medical decisions was low among family physicians.

CONCLUSIONS

This study indicated a positive perception among family physicians regarding AI integration into primary care settings. AI demonstrates significant potential for enhancing health care task management and overall service delivery at the PHCC. It augments family physicians' roles without replacing them and proves beneficial for operational efficiency, human resource management, and public health during pandemics. While the implementation of AI is anticipated to bring benefits, the careful consideration of ethical, privacy, confidentiality, and patient-centric concerns is essential. These insights provide valuable guidance for the strategic integration of AI into health care systems, with a focus on maintaining high-quality patient care and addressing the multifaceted challenges that arise during this transformative process.

Healthcare perceptions of persons with traumatic brain injuries across providers: shortcomings in the chronic phase of care

OBJECTIVE

This investigation sought to examine the perceptions of individuals with mild, moderate, and severe traumatic brain injury (TBIs) toward healthcare providers across rehabilitation contexts (acute, rehabilitation, and chronic).

METHODS

The mCARE-TBI survey was distributed via Qualtrics throughout the US and Canada. Sixty-four survey responses met criteria for analysis. Participants were ≥18 years old, one-year post onset, reported no unrelated neurological deficits prior to the single TBI, and reported no prior diagnosed psychiatric disorders. The mCARE was the primary outcome measure. Comparisons were made between provider ratings on each question.

RESULTS

Significant differences were found between chronic-phase care, compared to acute care and rehabilitation. Group differences were found in transitioning home after discharge and in between each therapy discipline and both nurses and doctors, as well as for really listening and pairwise differences between therapy disciplines and both nurses and doctors. Group effects were found for showing compassion and care and being positive.

CONCLUSIONS

All disciplines need to improve communication, and transition care/discharge planning. Dismissal of ongoing impairments continues to be an area of perceived concern with doctors in particular. Communication training is needed, particularly for nurses and doctors.

Empowering Healthcare through Comprehensive Informatics Education: The Status and Future of Biomedical and Health Informatics Education

OBJECTIVES

Education in biomedical and health informatics is essential for managing complex healthcare systems, bridging the gap between healthcare and information technology, and adapting to the digital requirements of the healthcare industry. This review presents the current status of biomedical and health informatics education domestically and internationally and proposes recommendations for future development.

METHODS

We analyzed evidence from reports and papers to explore global trends and international and domestic examples of education. The challenges and future strategies in Korea were also discussed based on the experts' opinions.

RESULTS

This review presents international recommendations for establishing education in biomedical and health informatics, as well as global examples at the undergraduate and graduate levels in medical and nursing education. It provides a thorough examination of the best practices, strategies, and competencies in informatics education. The review also assesses the current state of medical informatics and nursing informatics education in Korea. We highlight the challenges faced by academic institutions and conclude with a call to action for educators to enhance the preparation of professionals to effectively utilize technology in any healthcare setting.

CONCLUSIONS

To adapt to the digitalization of healthcare, systematic and continuous workforce development is essential. Future education should prioritize curriculum innovations and the establishment of integrated education programs, focusing not only on students but also on educators and all healthcare personnel in the field. Addressing these challenges requires collaboration among educational institutions, academic societies, government agencies, and international bodies dedicated to systematic and continuous workforce development.

Evaluation of ChatGPT for Patient Counseling in Kidney Stone Clinic: A Prospective Study

Introduction: The potential of large language models (LLMs) is to improve the clinical workflow and to make patient care efficient. We prospectively evaluated the performance of the LLM ChatGPT as a patient counseling tool in the urology stone clinic and validated the generated responses with those of urologists. Methods: We collected 61 questions from 12 kidney stone patients and prompted those to ChatGPT and a panel of experienced urologists (Level 1). Subsequently, the blinded responses of urologists and ChatGPT were presented to two expert urologists (Level 2) for comparative evaluation on preset domains: accuracy, relevance, empathy, completeness, and practicality. All responses were rated on a Likert scale of 1 to 10 for psychometric response evaluation. The mean difference in the scores given by the urologists (Level 2) was analyzed and interrater reliability (IRR) for the level of agreement in the responses between the urologists (Level 2) was analyzed by Cohen's kappa. Results: The mean differences in average scores between the responses from ChatGPT and urologists showed significant differences in accuracy (p < 0.001), empathy (p < 0.001), completeness (p < 0.001), and practicality (p < 0.001), except for the relevance domain (p = 0.051), with ChatGPT's responses being rated higher. The IRR analysis revealed significant agreement only in the empathy domain [k = 0.163, (0.059-0.266)]. Conclusion: We believe the introduction of ChatGPT in the clinical workflow could further optimize the information provided to patients in a busy stone clinic. In this preliminary study, ChatGPT supplemented the answers provided by the urologists, adding value to the conversation. However, in its current state, it is still not ready to be a direct source of authentic information for patients. We recommend its use as a source to build a comprehensive Frequently Asked Questions bank as a prelude to developing an LLM Chatbot for patient counseling.

A machine learning algorithm for peripheral artery disease prognosis using biomarker data

Peripheral artery disease (PAD) biomarkers have been studied in isolation; however, an algorithm that considers a protein panel to inform PAD prognosis may improve predictive accuracy. Biomarker-based prediction models were developed and evaluated using a model development (n = 270) and prospective validation cohort (n = 277). Plasma concentrations of 37 proteins were measured at baseline and the patients were followed for 2 years. The primary outcome was 2-year major adverse limb event (MALE; composite of vascular intervention or major amputation). Of the 37 proteins tested, 6 were differentially expressed in patients with vs. without PAD (ADAMTS13, ICAM-1, ANGPTL3, Alpha 1-microglobulin, GDF15, and endostatin). Using 10-fold cross-validation, we developed a random forest machine learning model that accurately predicts 2-year MALE in a prospective validation cohort of PAD patients using a 6-protein panel (AUROC 0.84). This algorithm can support PAD risk stratification, informing clinical decisions on further vascular evaluation and management.

Exploring the matrix: knowledge, perceptions and prospects of artificial intelligence and machine learning in Nigerian healthcare

Background

Artificial intelligence technology can be applied in several aspects of healthcare delivery and its integration into the Nigerian healthcare value chain is expected to bring about new opportunities. This study aimed at assessing the knowledge and perception of healthcare professionals in Nigeria regarding the application of artificial intelligence and machine learning in the health sector.

Methods

A cross-sectional study was undertaken amongst healthcare professionals in Nigeria with the use of a questionnaire. Data were collected across the six geopolitical zones in the Country using a stratified multistage sampling method. Descriptive and inferential statistical analyses were undertaken for the data obtained.

Results

Female participants (55.7%) were slightly higher in proportion compared to the male respondents (44.3%). Pharmacists accounted for 27.7% of the participants, and this was closely followed by medical doctors (24.5%) and nurses (19.3%). The majority of the respondents (57.2%) reported good knowledge regarding artificial intelligence and machine learning, about a third of the participants (32.2%) were of average knowledge, and 10.6% of the sample had poor knowledge. More than half of the respondents (57.8%) disagreed with the notion that the adoption of artificial intelligence in the Nigerian healthcare sector could result in job losses. Two-thirds of the participants (66.7%) were of the view that the integration of artificial intelligence in healthcare will augment human intelligence. Three-quarters (77%) of the respondents agreed that the use of machine learning in Nigerian healthcare could facilitate efficient service delivery.

Conclusion

This study provides novel insights regarding healthcare professionals' knowledge and perception with respect to the application of artificial intelligence and machine learning in healthcare. The emergent findings from this study can guide government and policymakers in decision-making as regards deployment of artificial intelligence and machine learning for healthcare delivery.

AI-driven decision support systems and epistemic reliance: a qualitative study on obstetricians' and midwives' perspectives on integrating AI-driven CTG into clinical decision making

BACKGROUND

Given that AI-driven decision support systems (AI-DSS) are intended to assist in medical decision making, it is essential that clinicians are willing to incorporate AI-DSS into their practice. This study takes as a case study the use of AI-driven cardiotography (CTG), a type of AI-DSS, in the context of intrapartum care. Focusing on the perspectives of obstetricians and midwives regarding the ethical and trust-related issues of incorporating AI-driven tools in their practice, this paper explores the conditions that AI-driven CTG must fulfill for clinicians to feel justified in incorporating this assistive technology into their decision-making processes regarding interventions in labor.

METHODS

This study is based on semi-structured interviews conducted online with eight obstetricians and five midwives based in England. Participants were asked about their current decision-making processes about when to intervene in labor, how AI-driven CTG might enhance or disrupt this process, and what it would take for them to trust this kind of technology. Interviews were transcribed verbatim and analyzed with thematic analysis. NVivo software was used to organize thematic codes that recurred in interviews to identify the issues that mattered most to participants. Topics and themes that were repeated across interviews were identified to form the basis of the analysis and conclusions of this paper.

RESULTS

There were four major themes that emerged from our interviews with obstetricians and midwives regarding the conditions that AI-driven CTG must fulfill: (1) the importance of accurate and efficient risk assessments; (2) the capacity for personalization and individualized medicine; (3) the lack of significance regarding the type of institution that develops technology; and (4) the need for transparency in the development process.

CONCLUSIONS

Accuracy, efficiency, personalization abilities, transparency, and clear evidence that it can improve outcomes are conditions that clinicians deem necessary for AI-DSS to meet in order to be considered reliable and therefore worthy of being incorporated into the decision-making process. Importantly, healthcare professionals considered themselves as the epistemic authorities in the clinical context and the bearers of responsibility for delivering appropriate care. Therefore, what mattered to them was being able to evaluate the reliability of AI-DSS on their own terms, and have confidence in implementing them in their practice.

ChatGPT as an aid for pathological diagnosis of cancer

Diagnostic workup of cancer patients is highly reliant on the science of pathology using cytopathology, histopathology, and other ancillary techniques like immunohistochemistry and molecular cytogenetics. Data processing and learning by means of artificial intelligence (AI) has become a spearhead for the advancement of medicine, with pathology and laboratory medicine being no exceptions. ChatGPT, an artificial intelligence (AI)-based chatbot, that was recently launched by OpenAI, is currently a talk of the town, and its role in cancer diagnosis is also being explored meticulously. Pathology workflow by integration of digital slides, implementation of advanced algorithms, and computer-aided diagnostic techniques extend the frontiers of the pathologist's view beyond a microscopic slide and enables effective integration, assimilation, and utilization of knowledge that is beyond human limits and boundaries. Despite of it's numerous advantages in the pathological diagnosis of cancer, it comes with several challenges like integration of digital slides with input language parameters, problems of bias, and legal issues which have to be addressed and worked up soon so that we as a pathologists diagnosing malignancies are on the same band wagon and don't miss the train.

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.

Can Artificial Intelligence Replace the Unique Nursing Role?

Artificial intelligence (AI) is transforming healthcare, offering potential benefits and challenges. In healthcare, AI enhances efficiency, streamlines processes, and supports decision-making. However, challenges include potential errors and biases in algorithms, data privacy concerns, legal and ethical issues, and resistance to change. In nursing, a delicate balance emerges between AI and human touch. While AI aids in data-driven decision-making and administrative tasks, it lacks the emotional intelligence, empathy, and nuanced understanding crucial to nursing care. Nurses excel in critical thinking, adaptability to dynamic situations, patient advocacy, collaboration, and establishing human connections. AI supports these functions by automating routine tasks and offering decision support tools, yet its rigidity in dynamic situations and lack of human touch pose limitations. This review underscores the necessity of careful AI integration in healthcare, acknowledging its advantages while mitigating drawbacks. In nursing, the symbiosis between AI and human qualities is vital. The role of AI should be to complement, not replace, the unique skills and empathetic aspects of nursing care. Addressing concerns related to bias, transparency, data privacy, and professional training is essential for maximizing the benefits of AI in healthcare while preserving the human touch in patient care. This article explores whether AI can replace unique nursing roles.

Fatty acid binding protein 4 has prognostic value in peripheral artery disease

OBJECTIVE

Peripheral artery disease (PAD) remains undertreated, despite its association with major amputation and mortality. This is partly due to a lack of available disease biomarkers. The intracellular protein fatty acid binding protein 4 (FABP4) is implicated in diabetes, obesity, and metabolic syndrome. Given that these risk factors are strong contributors to vascular disease, we assessed the prognostic ability of FABP4 in predicting PAD-related adverse limb events.

METHODS

This was a prospective case-control study with 3 years of follow-up. Baseline serum FABP4 concentrations were measured in patients with PAD (n = 569) and without PAD (n = 279). The primary outcome was major adverse limb event (MALE; defined as a composite of vascular intervention or major amputation). The secondary outcome was worsening PAD status (drop in ankle-brachial index ≥0.15). Kaplan-Meier and Cox proportional hazards analyses adjusted for baseline characteristics were conducted to assess the ability of FABP4 to predict MALE and worsening PAD status.

RESULTS

Patients with PAD were older and more likely to have cardiovascular risk factors compared with those without PAD. Over the study period, MALE and worsening PAD status occurred in 162 (19%) and 92 (11%) patients, respectively. Higher FABP4 levels were significantly associated with 3-year MALE (unadjusted hazard ratio [HR], 1.19; 95% confidence interval [CI], 1.04-1.27; adjusted HR, 1.18; 95% CI, 1.03-1.27; P = .022) and worsening PAD status (unadjusted HR, 1.18; 95% CI, 1.13-1.31; adjusted HR, 1.17; 95% CI, 1.12-1.28; P < .001). Three-year Kaplan-Meier survival analysis demonstrated that patients with high FABP4 levels had a decreased freedom from MALE (75% vs 88%; log rank = 22.6; P < .001), vascular intervention (77% vs 89%; log rank = 20.8; P < .001), and worsening PAD status (87% vs 91%; log rank = 6.16; P = .013).

CONCLUSIONS

Individuals with higher serum concentrations of FABP4 are more likely to develop PAD-related adverse limb events. FABP4 has prognostic value in risk-stratifying patients for further vascular evaluation and management.

Implementing AI in healthcare-the relevance of trust: a scoping review

Background

The process of translation of AI and its potential benefits into practice in healthcare services has been slow in spite of its rapid development. Trust in AI in relation to implementation processes is an important aspect. Without a clear understanding, the development of effective implementation strategies will not be possible, nor will AI advance despite the significant investments and possibilities.

Objective

This study aimed to explore the scientific literature regarding how trust in AI in relation to implementation in healthcare is conceptualized and what influences trust in AI in relation to implementation in healthcare.

Methods

This scoping review included five scientific databases. These were searched to identify publications related to the study aims. Articles were included if they were published in English, after 2012, and peer-reviewed. Two independent reviewers conducted an abstract and full-text review, as well as carrying out a thematic analysis with an inductive approach to address the study aims. The review was reported in accordance with the PRISMA-ScR guidelines.

Results

A total of eight studies were included in the final review. We found that trust was conceptualized in different ways. Most empirical studies had an individual perspective where trust was directed toward the technology's capability. Two studies focused on trust as relational between people in the context of the AI application rather than as having trust in the technology itself. Trust was also understood by its determinants and as having a mediating role, positioned between characteristics and AI use. The thematic analysis yielded three themes: individual characteristics, AI characteristics and contextual characteristics, which influence trust in AI in relation to implementation in healthcare.

Conclusions

Findings showed that the conceptualization of trust in AI differed between the studies, as well as which determinants they accounted for as influencing trust. Few studies looked beyond individual characteristics and AI characteristics. Future empirical research addressing trust in AI in relation to implementation in healthcare should have a more holistic view of the concept to be able to manage the many challenges, uncertainties, and perceived risks.

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

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

Perception of Pathologists in Poland of Artificial Intelligence and Machine Learning in Medical Diagnosis-A Cross-Sectional Study

BACKGROUND

In the past vicennium, several artificial intelligence (AI) and machine learning (ML) models have been developed to assist in medical diagnosis, decision making, and design of treatment protocols. The number of active pathologists in Poland is low, prolonging tumor patients' diagnosis and treatment journey. Hence, applying AI and ML may aid in this process. Therefore, our study aims to investigate the knowledge of using AI and ML methods in the clinical field in pathologists in Poland. To our knowledge, no similar study has been conducted.

METHODS

We conducted a cross-sectional study targeting pathologists in Poland from June to July 2022. The questionnaire included self-reported information on AI or ML knowledge, experience, specialization, personal thoughts, and level of agreement with different aspects of AI and ML in medical diagnosis. Data were analyzed using IBM^® SPSS^® Statistics v.26, PQStat Software v.1.8.2.238, and RStudio Build 351.

RESULTS

Overall, 68 pathologists in Poland participated in our study. Their average age and years of experience were 38.92 ± 8.88 and 12.78 ± 9.48 years, respectively. Approximately 42% used AI or ML methods, which showed a significant difference in the knowledge gap between those who never used it (OR = 17.9, 95% CI = 3.57-89.79, p < 0.001). Additionally, users of AI had higher odds of reporting satisfaction with the speed of AI in the medical diagnosis process (OR = 4.66, 95% CI = 1.05-20.78, p = 0.043). Finally, significant differences (p = 0.003) were observed in determining the liability for legal issues used by AI and ML methods.

CONCLUSION

Most pathologists in this study did not use AI or ML models, highlighting the importance of increasing awareness and educational programs regarding applying AI and ML in medical diagnosis.

The impact of artificial intelligence on the person-centred, doctor-patient relationship: some problems and solutions

Artificial intelligence (AI) is often cited as a possible solution to current issues faced by healthcare systems. This includes the freeing up of time for doctors and facilitating person-centred doctor-patient relationships. However, given the novelty of artificial intelligence tools, there is very little concrete evidence on their impact on the doctor-patient relationship or on how to ensure that they are implemented in a way which is beneficial for person-centred care.Given the importance of empathy and compassion in the practice of person-centred care, we conducted a literature review to explore how AI impacts these two values. Besides empathy and compassion, shared decision-making, and trust relationships emerged as key values in the reviewed papers. We identified two concrete ways which can help ensure that the use of AI tools have a positive impact on person-centred doctor-patient relationships. These are (1) using AI tools in an assistive role and (2) adapting medical education. The study suggests that we need to take intentional steps in order to ensure that the deployment of AI tools in healthcare has a positive impact on person-centred doctor-patient relationships. We argue that the proposed solutions are contingent upon clarifying the values underlying future healthcare systems.

The Role of Compassionate Care in Medicine: Toward Improving Patients' Quality of Care and Satisfaction

INTRODUCTION

Despite its description as a cornerstone of a healthcare provider's professional identity, the impact of compassionate care on various aspects of medicine has been poorly defined. In this review, we aimed to elucidate the role of compassionate care in various aspects of medicine and healthcare delivery.

METHODS

Four databases were searched using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol for a literature review regarding compassionate care and its intersection with medical education, patient-provider communication, patient care, and clinical outcomes, patient and provider characteristics, telemedicine and artificial intelligence, caregiver compassion fatigue, and cost of care.

RESULTS

Twenty-two articles met the inclusion criteria. Analysis revealed that clinical outcomes are correlated with the degree of patients' perception of empathy and compassion from their providers. Along with enhanced patient outcomes, compassionate care was shown to reduce the costs of care, compassion fatigue and burnout, and the number of malpractice claims. However, compassion can be perceived differently among patients of various cultural and ethnic backgrounds. Compassion training sessions can be implemented among residents in surgical and nonsurgical medical specialties to improve perceived compassion. Furthermore, the use of telehealth modalities may positively or negatively impact compassionate care, requiring further exploration.

CONCLUSIONS

Compassionate care plays a crucial role in improving patient care and clinical outcomes while reducing caregiver burnout and the risk of malpractice litigation. However, a lack of compassion training and caregiver compassion fatigue may detract from the delivery of effective compassionate care.

Preparing for an Artificial Intelligence-Enabled Future: Patient Perspectives on Engagement and Health Care Professional Training for Adopting Artificial Intelligence Technologies in Health Care Settings

BACKGROUND

As new technologies emerge, there is a significant shift in the way care is delivered on a global scale. Artificial intelligence (AI) technologies have been rapidly and inexorably used to optimize patient outcomes, reduce health system costs, improve workflow efficiency, and enhance population health. Despite the widespread adoption of AI technologies, the literature on patient engagement and their perspectives on how AI will affect clinical care is scarce. Minimal patient engagement can limit the optimization of these novel technologies and contribute to suboptimal use in care settings.

OBJECTIVE

We aimed to explore patients' views on what skills they believe health care professionals should have in preparation for this AI-enabled future and how we can better engage patients when adopting and deploying AI technologies in health care settings.

METHODS

Semistructured interviews were conducted from August 2020 to December 2021 with 12 individuals who were a patient in any Canadian health care setting. Interviews were conducted until thematic saturation occurred. A thematic analysis approach outlined by Braun and Clarke was used to inductively analyze the data and identify overarching themes.

RESULTS

Among the 12 patients interviewed, 8 (67%) were from urban settings and 4 (33%) were from rural settings. A majority of the participants were very comfortable with technology (n=6, 50%) and somewhat familiar with AI (n=7, 58%). In total, 3 themes emerged: cultivating patients' trust, fostering patient engagement, and establishing data governance and validation of AI technologies.

CONCLUSIONS

With the rapid surge of AI solutions, there is a critical need to understand patient values in advancing the quality of care and contributing to an equitable health system. Our study demonstrated that health care professionals play a synergetic role in the future of AI and digital technologies. Patient engagement is vital in addressing underlying health inequities and fostering an optimal care experience. Future research is warranted to understand and capture the diverse perspectives of patients with various racial, ethnic, and socioeconomic backgrounds.

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.

Artificial intelligence technologies and compassion in healthcare: A systematic scoping review

Background

Advances in artificial intelligence (AI) technologies, together with the availability of big data in society, creates uncertainties about how these developments will affect healthcare systems worldwide. Compassion is essential for high-quality healthcare and research shows how prosocial caring behaviors benefit human health and societies. However, the possible association between AI technologies and compassion is under conceptualized and underexplored.

Objectives

The aim of this scoping review is to provide a comprehensive depth and a balanced perspective of the emerging topic of AI technologies and compassion, to inform future research and practice. The review questions were: How is compassion discussed in relation to AI technologies in healthcare? How are AI technologies being used to enhance compassion in healthcare? What are the gaps in current knowledge and unexplored potential? What are the key areas where AI technologies could support compassion in healthcare?

Materials and methods

A systematic scoping review following five steps of Joanna Briggs Institute methodology. Presentation of the scoping review conforms with PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews). Eligibility criteria were defined according to 3 concept constructs (AI technologies, compassion, healthcare) developed from the literature and informed by medical subject headings (MeSH) and key words for the electronic searches. Sources of evidence were Web of Science and PubMed databases, articles published in English language 2011-2022. Articles were screened by title/abstract using inclusion/exclusion criteria. Data extracted (author, date of publication, type of article, aim/context of healthcare, key relevant findings, country) was charted using data tables. Thematic analysis used an inductive-deductive approach to generate code categories from the review questions and the data. A multidisciplinary team assessed themes for resonance and relevance to research and practice.

Results

Searches identified 3,124 articles. A total of 197 were included after screening. The number of articles has increased over 10 years (2011, n = 1 to 2021, n = 47 and from Jan-Aug 2022 n = 35 articles). Overarching themes related to the review questions were: (1) Developments and debates (7 themes) Concerns about AI ethics, healthcare jobs, and loss of empathy; Human-centered design of AI technologies for healthcare; Optimistic speculation AI technologies will address care gaps; Interrogation of what it means to be human and to care; Recognition of future potential for patient monitoring, virtual proximity, and access to healthcare; Calls for curricula development and healthcare professional education; Implementation of AI applications to enhance health and wellbeing of the healthcare workforce. (2) How AI technologies enhance compassion (10 themes) Empathetic awareness; Empathetic response and relational behavior; Communication skills; Health coaching; Therapeutic interventions; Moral development learning; Clinical knowledge and clinical assessment; Healthcare quality assessment; Therapeutic bond and therapeutic alliance; Providing health information and advice. (3) Gaps in knowledge (4 themes) Educational effectiveness of AI-assisted learning; Patient diversity and AI technologies; Implementation of AI technologies in education and practice settings; Safety and clinical effectiveness of AI technologies. (4) Key areas for development (3 themes) Enriching education, learning and clinical practice; Extending healing spaces; Enhancing healing relationships.

Conclusion

There is an association between AI technologies and compassion in healthcare and interest in this association has grown internationally over the last decade. In a range of healthcare contexts, AI technologies are being used to enhance empathetic awareness; empathetic response and relational behavior; communication skills; health coaching; therapeutic interventions; moral development learning; clinical knowledge and clinical assessment; healthcare quality assessment; therapeutic bond and therapeutic alliance; and to provide health information and advice. The findings inform a reconceptualization of compassion as a human-AI system of intelligent caring comprising six elements: (1) Awareness of suffering (e.g., pain, distress, risk, disadvantage); (2) Understanding the suffering (significance, context, rights, responsibilities etc.); (3) Connecting with the suffering (e.g., verbal, physical, signs and symbols); (4) Making a judgment about the suffering (the need to act); (5) Responding with an intention to alleviate the suffering; (6) Attention to the effect and outcomes of the response. These elements can operate at an individual (human or machine) and collective systems level (healthcare organizations or systems) as a cyclical system to alleviate different types of suffering. New and novel approaches to human-AI intelligent caring could enrich education, learning, and clinical practice; extend healing spaces; and enhance healing relationships.

Implications

In a complex adaptive system such as healthcare, human-AI intelligent caring will need to be implemented, not as an ideology, but through strategic choices, incentives, regulation, professional education, and training, as well as through joined up thinking about human-AI intelligent caring. Research funders can encourage research and development into the topic of AI technologies and compassion as a system of human-AI intelligent caring. Educators, technologists, and health professionals can inform themselves about the system of human-AI intelligent caring.

Public Trust in Dentists among Arabic Populations

Objective

To investigate public trust in dentists, fear of dentists, factors related to trust, and the impact of the COVID-19 pandemic on the trust in dentists.

Materials and Methods

We used an Arabic online anonymous survey to collect data from a random population of 838 adults to investigate public trust in dentists, the factors they perceive to affect trust, their perception of key factors in the dentist-patient relationship, fear of dentists, and the impact of the COVID-19 pandemic on the level of their trust in the dentists.

Results

Eight hundred thirty-eight subjects with a mean age of 28.5 responded to the survey (595 (71%) females, 235 (28%) males, and 8 (1%) did not specify their gender). More than half trust their dentist. The COVID-19 pandemic did not decrease trust in dentists according to 62.2%. There were significant gender differences in reporting fear of dentists (p < 0.001) and in the perception of factors affecting trust (p=0.028). Honesty was chosen by 583 (69.6%), competence by 549 (65.5), and dentist's reputation by 443 (52.9%).

Conclusions

The findings of this study show that the majority of the public trust dentists, more females reported fear of dentists, and the majority perceived honesty, competence, and reputation as key factors affecting trust in the dentist-patient relationship. The majority reported that the COVID-19 pandemic did not have a negative impact on their trust in dentists.

Elder's life course theory and person-centered research: A lens for conducting ethical nursing research and mental health nursing practice with older adults aging with the diagnosis of schizophrenia

INTRODUCTION

Older adults diagnosed with schizophrenia are a vulnerable population owing to the manifestations of their illness, which can include decreased reality orientation, paranoia, hallucinations and delusions. This paper presents ethical principles of vulnerability, veracity, non-maleficence and autonomy for person-centered care in mental health nursing research and practice, focused with the lens of Elder's life course theory (LCT).

AIM

To present Elder's LCT as an ethical lens for person-centered care as nurses engage with older adults aging with the diagnosis of schizophrenia in clinical practice and/or research.

METHOD

Four ethical principles fundamental to nursing research and mental health practice are presented, with Elder's LCT as a theoretical lens for person-centered care.

RESULTS

A model for ethical research and mental health practice with older adults diagnosed with schizophrenia.

DISCUSSION

Nursing research and mental health nursing practice with an ethical LCT lens for person-centered can help nurses envision, explore and generate interventions to address the special needs of older adults aging with schizophrenia.

IMPLICATIONS FOR PRACTICE

The use of a LCT lens for person-centered care can encourage nurses in research and mental health practice to seek information collaboratively with older adults diagnosed with schizophrenia in a thoughtful, ethical manner, to inform the improvement of their health outcomes and health policy.

Considering Clinician Competencies for the Implementation of Artificial Intelligence–Based Tools in Health Care: Findings From a Scoping Review

Background

The use of artificial intelligence (AI)–based tools in the care of individual patients and patient populations is rapidly expanding.

Objective

The aim of this paper is to systematically identify research on provider competencies needed for the use of AI in clinical settings.

Methods

A scoping review was conducted to identify articles published between January 1, 2009, and May 1, 2020, from MEDLINE, CINAHL, and the Cochrane Library databases, using search queries for terms related to health care professionals (eg, medical, nursing, and pharmacy) and their professional development in all phases of clinical education, AI-based tools in all settings of clinical practice, and professional education domains of competencies and performance. Limits were provided for English language, studies on humans with abstracts, and settings in the United States.

Results

The searches identified 3476 records, of which 4 met the inclusion criteria. These studies described the use of AI in clinical practice and measured at least one aspect of clinician competence. While many studies measured the performance of the AI-based tool, only 4 measured clinician performance in terms of the knowledge, skills, or attitudes needed to understand and effectively use the new tools being tested. These 4 articles primarily focused on the ability of AI to enhance patient care and clinical decision-making by improving information flow and display, specifically for physicians.

Conclusions

While many research studies were identified that investigate the potential effectiveness of using AI technologies in health care, very few address specific competencies that are needed by clinicians to use them effectively. This highlights a critical gap.

Weak inclusion of the medical humanities in medical education: a qualitative study among Danish medical students

BACKGROUND

The modern medical education is predominantly grounded in the biomedical sciences. In recent years, medical humanities have been included into the medical curricula in many countries around the world one of the objectives being to promote patient-centred, empathic care by future physicians. Studies have been made of the impact of inclusion of medical humanities components within the medical curriculum. Although some results suggest increased empathy, others remain inconclusive. To gain insight into the depth, context, and impact of inclusion of the medical humanities for future physicians, this study aimed to explore Danish medical students' understanding of and reflections on how the medical humanities relate to the medical education, including the clinic.

METHODS

We conducted a qualitative research study, involving semi-structured interviews with twenty-three Danish medical students across years of curriculum and medical schools. Interviews were recorded, transcribed verbatim and analyzed using Braun and Clarke's thematic analysis.

RESULTS

The findings demonstrate the subordinate role of the medical humanities in the medical educational system. Students prioritize biomedical knowledge building in the preclinical curriculum, partly as a reaction to an unbalanced institutional inclusion of the medical humanities. Observing how structural empathy incentives are lacking in the clinical curriculum, the values inherent in the medical humanities are undermined.

CONCLUSION

Danish medical students become part of an educational environment with lacking institutional conditions and structures to promote the strong inclusion of the medical humanities. A focus is therefore needed on the values, norms and structures of the medical educational systems that undermine a strong inclusion of the medical humanities into medical education.

Artificial intelligence in gastroenterology and hepatology: how to advance clinical practice while ensuring health equity

Artificial intelligence (AI) and machine learning (ML) systems are increasingly used in medicine to improve clinical decision-making and healthcare delivery. In gastroenterology and hepatology, studies have explored a myriad of opportunities for AI/ML applications which are already making the transition to bedside. Despite these advances, there is a risk that biases and health inequities can be introduced or exacerbated by these technologies. If unrecognised, these technologies could generate or worsen systematic racial, ethnic and sex disparities when deployed on a large scale. There are several mechanisms through which AI/ML could contribute to health inequities in gastroenterology and hepatology, including diagnosis of oesophageal cancer, management of inflammatory bowel disease (IBD), liver transplantation, colorectal cancer screening and many others. This review adapts a framework for ethical AI/ML development and application to gastroenterology and hepatology such that clinical practice is advanced while minimising bias and optimising health equity.

Toward an Ecologically Valid Conceptual Framework for the Use of Artificial Intelligence in Clinical Settings: Need for Systems Thinking, Accountability, Decision-making, Trust, and Patient Safety Considerations in Safeguarding the Technology and Clinicians

The health care management and the medical practitioner literature lack a descriptive conceptual framework for understanding the dynamic and complex interactions between clinicians and artificial intelligence (AI) systems. As most of the existing literature has been investigating AI's performance and effectiveness from a statistical (analytical) standpoint, there is a lack of studies ensuring AI's ecological validity. In this study, we derived a framework that focuses explicitly on the interaction between AI and clinicians. The proposed framework builds upon well-established human factors models such as the technology acceptance model and expectancy theory. The framework can be used to perform quantitative and qualitative analyses (mixed methods) to capture how clinician-AI interactions may vary based on human factors such as expectancy, workload, trust, cognitive variables related to absorptive capacity and bounded rationality, and concerns for patient safety. If leveraged, the proposed framework can help to identify factors influencing clinicians' intention to use AI and, consequently, improve AI acceptance and address the lack of AI accountability while safeguarding the patients, clinicians, and AI technology. Overall, this paper discusses the concepts, propositions, and assumptions of the multidisciplinary decision-making literature, constituting a sociocognitive approach that extends the theories of distributed cognition and, thus, will account for the ecological validity of AI.

The Future Ethics of Artificial Intelligence in Medicine: Making Sense of Collaborative Models

This article examines the role of medical doctors, AI designers, and other stakeholders in making applied AI and machine learning ethically acceptable on the general premises of shared decision-making in medicine. Recent policy documents such as the EU strategy on trustworthy AI and the research literature have often suggested that AI could be made ethically acceptable by increased collaboration between developers and other stakeholders. The article articulates and examines four central alternative models of how AI can be designed and applied in patient care, which we call the ordinary evidence model, the ethical design model, the collaborative model, and the public deliberation model. We argue that the collaborative model is the most promising for covering most AI technology, while the public deliberation model is called for when the technology is recognized as fundamentally transforming the conditions for ethical shared decision-making.

The Impact of Healthcare 4.0 on the Healthcare Service Quality: A Systematic Literature Review

Healthcare 4.0 is inspired by Industry 4.0 and its application has resulted in a paradigmatic shift in the field of healthcare. However, the impact of this digital revolution in the healthcare system on healthcare service quality is not known. The purpose of this study is to examine the impact of healthcare 4.0 on healthcare service quality. This study used the systematic literature review methodology suggested by Transfield et al. to critically examine 67 articles. The impact of healthcare 4.0 is analyzed in-depth in terms of the interpersonal, technical, environmental, and administrative aspect of healthcare service quality. This study will be useful to hospitals and other stakeholders to understand the impact of healthcare 4.0 on the service quality of health systems. Besides, this study critically analyses the existing literature and identifies research areas in this field and hence will be beneficial to researchers. Though there are few literature reviews in healthcare 4.0, this is the first study to examine the impact of Healthcare 4.0 on healthcare service quality.

Ethical, legal, and social considerations of AI-based medical decision-support tools: A scoping review

INTRODUCTION

Recent developments in the field of Artificial Intelligence (AI) applied to healthcare promise to solve many of the existing global issues in advancing human health and managing global health challenges. This comprehensive review aims not only to surface the underlying ethical and legal but also social implications (ELSI) that have been overlooked in recent reviews while deserving equal attention in the development stage, and certainly ahead of implementation in healthcare. It is intended to guide various stakeholders (eg. designers, engineers, clinicians) in addressing the ELSI of AI at the design stage using the Ethics by Design (EbD) approach.

METHODS

The authors followed a systematised scoping methodology and searched the following databases: Pubmed, Web of science, Ovid, Scopus, IEEE Xplore, EBSCO Search (Academic Search Premier, CINAHL, PSYCINFO, APA PsycArticles, ERIC) for the ELSI of AI in healthcare through January 2021. Data were charted and synthesised, and the authors conducted a descriptive and thematic analysis of the collected data.

RESULTS

After reviewing 1108 papers, 94 were included in the final analysis. Our results show a growing interest in the academic community for ELSI in the field of AI. The main issues of concern identified in our analysis fall into four main clusters of impact: AI algorithms, physicians, patients, and healthcare in general. The most prevalent issues are patient safety, algorithmic transparency, lack of proper regulation, liability & accountability, impact on patient-physician relationship and governance of AI empowered healthcare.

CONCLUSIONS

The results of our review confirm the potential of AI to significantly improve patient care, but the drawbacks to its implementation relate to complex ELSI that have yet to be addressed. Most ELSI refer to the impact on and extension of the reciprocal and fiduciary patient-physician relationship. With the integration of AIbased decision making tools, a bilateral patient-physician relationship may shift into a trilateral one.

How Can Research on Artificial Empathy Be Enhanced by Applying Deepfakes?

We propose the idea of using an open data set of doctor-patient interactions to develop artificial empathy based on facial emotion recognition. Facial emotion recognition allows a doctor to analyze patients' emotions, so that they can reach out to their patients through empathic care. However, face recognition data sets are often difficult to acquire; many researchers struggle with small samples of face recognition data sets. Further, sharing medical images or videos has not been possible, as this approach may violate patient privacy. The use of deepfake technology is a promising approach to deidentifying video recordings of patients’ clinical encounters. Such technology can revolutionize the implementation of facial emotion recognition by replacing a patient's face in an image or video with an unrecognizable face—one with a facial expression that is similar to that of the original. This technology will further enhance the potential use of artificial empathy in helping doctors provide empathic care to achieve good doctor-patient therapeutic relationships, and this may result in better patient satisfaction and adherence to treatment.

Development of Rasmussen's risk management framework for analysing multi-level sociotechnical influences in the design of envisioned work systems

Besides radically altering work, advances in automation and intelligent technologies have the potential to bring significant societal transformation. These transitional periods require an approach to analysis and design that goes beyond human-machine interaction in the workplace to consider the wider sociotechnical needs of envisioned work systems. The Sociotechnical Influences Space, an analytical tool motivated by Rasmussen's risk management model, promotes a holistic approach to the design of future systems, attending to societal needs and challenges, while still recognising the bottom-up push from emerging technologies. A study explores the concept and practical potential of the tool when applied to the analysis of a large-scale, 'real-world' problem, specifically the societal, governmental, regulatory, organisational, human, and technological factors of significance in mixed human-artificial agent workforces. Further research is needed to establish the feasibility of the tool in a range of application domains, the details of the method, and the value of the tool in design. Practitioner summary: Emerging automation and intelligent technologies are not only transforming workplaces, but may be harbingers of major societal change. A new analytical tool, the Sociotechnical Influences Space, is proposed to support organisations in taking a holistic approach to the incorporation of advanced technologies into workplaces and function allocation in mixed human-artificial agent teams.

Caring in the in-between: a proposal to introduce responsible AI and robotics to healthcare

In the scenario of growing polarization of promises and dangers that surround artificial intelligence (AI), how to introduce responsible AI and robotics in healthcare? In this paper, we develop an ethical–political approach to introduce democratic mechanisms to technological development, what we call “Caring in the In-Between”. Focusing on the multiple possibilities for action that emerge in the realm of uncertainty, we propose an ethical and responsible framework focused on care actions in between fears and hopes. Using the theoretical perspective of Science and Technology Studies and empirical research, “Caring in the In-Between” is based on three movements: the first is a change of focus from the world of promises and dangers to the world of uncertainties; the second is a conceptual shift from assuming a relationship with robotics based on a Human–Robot Interaction to another focused on the network in which the robot is embedded (the “Robot Embedded in a Network”); and the last is an ethical shift from a general normative framework to a discussion on the context of use. Based on these suggestions, “Caring in the In-Between” implies institutional challenges, as well as new practices in healthcare systems. It is articulated around three simultaneous processes, each of them related to practical actions in the “in-between” dimensions considered: monitoring relations and caring processes, through public engagement and institutional changes; including concerns and priorities of stakeholders, with the organization of participatory processes and alternative forms of representation; and making fears and hopes commensurable, through the choice of progressive and reversible actions.

Patients' Perceptions Toward Human-Artificial Intelligence Interaction in Health Care: Experimental Study

Background

It is believed that artificial intelligence (AI) will be an integral part of health care services in the near future and will be incorporated into several aspects of clinical care such as prognosis, diagnostics, and care planning. Thus, many technology companies have invested in producing AI clinical applications. Patients are one of the most important beneficiaries who potentially interact with these technologies and applications; thus, patients’ perceptions may affect the widespread use of clinical AI. Patients should be ensured that AI clinical applications will not harm them, and that they will instead benefit from using AI technology for health care purposes. Although human-AI interaction can enhance health care outcomes, possible dimensions of concerns and risks should be addressed before its integration with routine clinical care.

Objective

The main objective of this study was to examine how potential users (patients) perceive the benefits, risks, and use of AI clinical applications for their health care purposes and how their perceptions may be different if faced with three health care service encounter scenarios.

Methods

We designed a 2×3 experiment that crossed a type of health condition (ie, acute or chronic) with three different types of clinical encounters between patients and physicians (ie, AI clinical applications as substituting technology, AI clinical applications as augmenting technology, and no AI as a traditional in-person visit). We used an online survey to collect data from 634 individuals in the United States.

Results

The interactions between the types of health care service encounters and health conditions significantly influenced individuals’ perceptions of privacy concerns, trust issues, communication barriers, concerns about transparency in regulatory standards, liability risks, benefits, and intention to use across the six scenarios. We found no significant differences among scenarios regarding perceptions of performance risk and social biases.

Conclusions

The results imply that incompatibility with instrumental, technical, ethical, or regulatory values can be a reason for rejecting AI applications in health care. Thus, there are still various risks associated with implementing AI applications in diagnostics and treatment recommendations for patients with both acute and chronic illnesses. The concerns are also evident if the AI applications are used as a recommendation system under physician experience, wisdom, and control. Prior to the widespread rollout of AI, more studies are needed to identify the challenges that may raise concerns for implementing and using AI applications. This study could provide researchers and managers with critical insights into the determinants of individuals’ intention to use AI clinical applications. Regulatory agencies should establish normative standards and evaluation guidelines for implementing AI in health care in cooperation with health care institutions. Regular audits and ongoing monitoring and reporting systems can be used to continuously evaluate the safety, quality, transparency, and ethical factors of AI clinical applications.

The Social Robot and the Digital Physiotherapist: Are We Ready for the Team Play?

Motivation: We are witnessing two phenomena. The first is that the physiotherapist is increasingly becoming a figure that must interact with Digital Health. On the other hand, social robots through research are improving more and more in the aspects of social interaction thanks also to artificial intelligence and becoming useful in rehabilitation processes. It begins to become strategic to investigate the intersections between these two phenomena. Objective: Therefore, we set ourselves the goal of investigating the consensus and opinion of physiotherapists around the introduction of social robots in clinical practice both in rehabilitation and assistance. Procedure: An electronic survey has been developed focused on social robot-based rehabilitation and assistance and has been submitted to subjects focused on physiotherapy sciences to investigate their opinion and their level of consent regarding the use of the social robot in rehabilitation and assistance. Two samples of subjects were recruited: the first group (156 participating subjects, 79 males, 77 females, mean age 24.3 years) was in the training phase, and the second (167 participating subjects, 86 males, 81 females, mean age 42.4 years) group was involved in the work processes. An electronic feedback form was also submitted to investigate the acceptance of the proposed methodology. Results: The survey showed a consistency of the results between the two samples from which interesting considerations emerge. Contrary to stereotypes that report how AI-based devices put jobs at risk, physiotherapists are not afraid of these devices. The subjects involved in the study believe the following: (a) social robots can be reliable co-workers but will remain a complementary device; (b) their role will be of the utmost importance as an operational manager in their use and in performance monitoring; (c) these devices will allow an increase in working capacity and facilitate integration. All those involved in the study believe that the proposed electronic survey has proved to be a useful and effective tool that can be useful as a periodic monitoring tool and useful for scientific societies. Conclusions: The evolution of social robots represents an unstoppable process as does the increase in the aging of the population. Stakeholders must not look with suspicion toward these devices, which can represent an important resource, but rather invest in monitoring and consensus training initiatives.

Ethics of artificial intelligence in global health: Explainability, algorithmic bias and trust

AI has the potential to disrupt and transform the way we deliver care globally. It is reputed to be able to improve the accuracy of diagnoses and treatments, and make the provision of services more efficient and effective. In surgery, AI systems could lead to more accurate diagnoses of health problems and help surgeons better care for their patients. In the context of lower-and-middle-income-countries (LMICs), where access to healthcare still remains a global problem, AI could facilitate access to healthcare professionals and services, even specialist services, for millions of people. The ability of AI to deliver on its promises, however, depends on successfully resolving the ethical and practical issues identified, including that of explainability and algorithmic bias. Even though such issues might appear as being merely practical or technical ones, their closer examination uncovers questions of value, fairness and trust. It should not be left to AI developers, being research institutions or global tech companies, to decide how to resolve these ethical questions. Particularly, relying only on the trustworthiness of companies and institutions to address ethical issues relating to justice, fairness and health equality would be unsuitable and unwise. The pathway to a fair, appropriate and relevant AI necessitates the development, and critically, successful implementation of national and international rules and regulations that define the parameters and set the boundaries of operation and engagement.

Virtual Reality as a Tool for Eliciting Empathetic Behaviour in Carers: An Integrative Review

BACKGROUND

Empathy is an essential behavioural competency required of healthcare providers. It is included in the Canadian National Competency Profile for Medical Radiation Technologists; supporting patient-centered care and the development of therapeutic relationships between carers and care recipients. This review synthesizes evidence on Virtual Reality (VR) as a tool for eliciting empathetic behaviour in carers.

METHODS

Six Electronic databases (PubMed, EMBASE, PsycInfo, ERIC, Academic Search Premier and CINAHL) were searched for studies published from 1997-2020. Studies with MeSH terms "Empathy" and "Virtual Reality", and conducted on adult carers, were eligible for inclusion. The Cochrane risk of bias tool guided the assessment of study quality. Prospero review registration number: CRD42020210049 RESULTS: Seven studies, with 485 carers, were included in this review. Studies were heterogeneous in terms of interventions and tools for collecting outcome measures. All seven studies demonstrated VR could elicit empathetic behaviour in carers. Studies included one randomized controlled trial, three non-randomized controlled trials, two with quasi-experimental designs, and one non-experimental design. All studies had a moderate to high risk of bias.

CONCLUSION

This review suggests that VR may be an appropriate method for eliciting empathetic behaviours in carers. Future studies employing appropriately powered multicentere randomized controlled designs should seek to determine which VR experiences are the most effective in evoking empathetic behaviours.

Older Adults Diagnosed With Schizophrenia in Long-term Care Facilities: Life Course Theory for Holistic Nursing Research and Practice

Older adults diagnosed with schizophrenia (OADWS) often enter long-term care facilities with unique challenges related to trauma and stress experienced throughout their life course. Health care workers often report that when they work with this population, they feel unprepared due to limited training. In this article, life course theory is presented as a lens for holistic nursing research and as a way for nurses to adapt interventions already used with cognitively impaired older adults (e.g., those diagnosed with Alzheimer's disease) for OADWS in long-term care. It is hoped that these ideas will facilitate discussion of ways to inform training for holistic long-term care of OADWS. Holistic principles of nursing addressed with life course theory as a lens include the following: (a) accounting for strengths and challenges; (b) honoring experiences, values, and health beliefs; (c) viewing interrelationships with the environment; and (d) nurturing of peace, wholeness, and healing.

Applications of artificial intelligence for hypertension management

The prevalence of hypertension is increasing along with an aging population, causing millions of premature deaths annually worldwide. Low awareness of blood pressure (BP) elevation and suboptimal hypertension diagnosis serve as the major hurdles in effective hypertension management. The advent of artificial intelligence (AI), however, sheds the light of new strategies for hypertension management, such as remote supports from telemedicine and big data-derived prediction. There is considerable evidence demonstrating the feasibility of AI applications in hypertension management. A foreseeable trend was observed in integrating BP measurements with various wearable sensors and smartphones, so as to permit continuous and convenient monitoring. In the meantime, further investigations are advised to validate the novel prediction and prognostic tools. These revolutionary developments have made a stride toward the future model for digital management of chronic diseases.

The Sustainability of Artificial Intelligence: An Urbanistic Viewpoint from the Lens of Smart and Sustainable Cities

The popularity and application of artificial intelligence (AI) are increasing rapidly all around the world—where, in simple terms, AI is a technology which mimics the behaviors commonly associated with human intelligence. Today, various AI applications are being used in areas ranging from marketing to banking and finance, from agriculture to healthcare and security, from space exploration to robotics and transport, and from chatbots to artificial creativity and manufacturing. More recently, AI applications have also started to become an integral part of many urban services. Urban artificial intelligences manage the transport systems of cities, run restaurants and shops where every day urbanity is expressed, repair urban infrastructure, and govern multiple urban domains such as traffic, air quality monitoring, garbage collection, and energy. In the age of uncertainty and complexity that is upon us, the increasing adoption of AI is expected to continue, and so its impact on the sustainability of our cities. This viewpoint explores and questions the sustainability of AI from the lens of smart and sustainable cities, and generates insights into emerging urban artificial intelligences and the potential symbiosis between AI and a smart and sustainable urbanism. In terms of methodology, this viewpoint deploys a thorough review of the current status of AI and smart and sustainable cities literature, research, developments, trends, and applications. In so doing, it contributes to existing academic debates in the fields of smart and sustainable cities and AI. In addition, by shedding light on the uptake of AI in cities, the viewpoint seeks to help urban policymakers, planners, and citizens make informed decisions about a sustainable adoption of AI.