Ethical issues in using ambient intelligence in health-care settings

Ethics of Wearable-Based Out-of-Hospital Cardiac Arrest Detection

Out-of-hospital cardiac arrest is a major health problem, and immediate treatment is essential for improving the chances of survival. The development of technological solutions to detect out-of-hospital cardiac arrest and alert emergency responders is gaining momentum; multiple research consortia are currently developing wearable technology for this purpose. For the responsible design and implementation of this technology, it is necessary to attend to the ethical implications. This review identifies relevant ethical aspects of wearable-based out-of-hospital cardiac arrest detection according to four key principles of medical ethics. First, aspects related to beneficence concern the effectiveness of the technology. Second, nonmaleficence requires preventing psychological distress associated with wearing the device and raises questions about the desirability of screening. Third, grounded in autonomy are empowerment, the potential reidentification from continuously collected data, issues of data access, bystander privacy, and informed consent. Finally, justice concerns include the risks of algorithmic bias and unequal technology access. Based on this overview and relevant legislation, we formulate design recommendations. We suggest that key elements are device accuracy and reliability, dynamic consent, purpose limitation, and personalization. Further empirical research is needed into the perspectives of stakeholders, including people at risk of out-of-hospital cardiac arrest and their next-of-kin, to achieve a successful and ethically balanced integration of this technology in society.

Gaps in the Global Regulatory Frameworks for the Use of Artificial Intelligence (AI) in the Healthcare Services Sector and Key Recommendations

Artificial Intelligence (AI) has shown remarkable potential to revolutionise healthcare by enhancing diagnostics, improving treatment outcomes, and streamlining administrative processes. In the global regulatory landscape, several countries are working on regulating AI in healthcare. There are five key regulatory issues that need to be addressed: (i) data security and protection-measures to cover the "digital health footprints" left unknowingly by patients when they access AI in health services; (ii) data quality-availability of safe and secure data and more open database sources for AI, algorithms, and datasets to ensure equity and prevent demographic bias; (iii) validation of algorithms-mapping of the explainability and causability of the AI system; (iv) accountability-whether this lies with the healthcare professional, healthcare organisation, or the personified AI algorithm; (v) ethics and equitable access-whether fundamental rights of people are met in an ethical manner. Policymakers may need to consider the entire life cycle of AI in healthcare services and the databases that were used for the training of the AI system, along with requirements for their risk assessments to be publicly accessible for effective regulatory oversight. AI services that enhance their functionality over time need to undergo repeated algorithmic impact assessment and must also demonstrate real-time performance. Harmonising regulatory frameworks at the international level would help to resolve cross-border issues of AI in healthcare services.

Effect of Ambient Voice Technology, Natural Language Processing, and Artificial Intelligence on the Patient-Physician Relationship

BACKGROUND

 The method of documentation during a clinical encounter may affect the patient-physician relationship.

OBJECTIVES

 Evaluate how the use of ambient voice recognition, coupled with natural language processing and artificial intelligence (DAX), affects the patient-physician relationship.

METHODS

 This was a prospective observational study with a primary aim of evaluating any difference in patient satisfaction on the Patient-Doctor Relationship Questionnaire-9 (PDRQ-9) scale between primary care encounters in which DAX was utilized for documentation as compared to another method. A single-arm open-label phase was also performed to query direct feedback from patients.

RESULTS

 A total of 288 patients were include in the open-label arm and 304 patients were included in the masked phase of the study comparing encounters with and without DAX use. In the open-label phase, patients strongly agreed that the provider was more focused on them, spent less time typing, and made the encounter feel more personable. In the masked phase of the study, no difference was seen in the total PDRQ-9 score between patients whose encounters used DAX (median: 45, interquartile range [IQR]: 8) and those who did not (median: 45 [IQR: 3.5]; p = 0.31). The adjusted odds ratio for DAX use was 0.8 (95% confidence interval: 0.48-1.34) for the patient reporting complete satisfaction on how well their clinician listened to them during their encounter.

CONCLUSION

 Patients strongly agreed with the use of ambient voice recognition, coupled with natural language processing and artificial intelligence (DAX) for documentation in primary care. However, no difference was detected in the patient-physician relationship on the PDRQ-9 scale.

Depth over RGB: automatic evaluation of open surgery skills using depth camera

PURPOSE

In this paper, we present a novel approach to the automatic evaluation of open surgery skills using depth cameras. This work is intended to show that depth cameras achieve similar results to RGB cameras, which is the common method in the automatic evaluation of open surgery skills. Moreover, depth cameras offer advantages such as robustness to lighting variations, camera positioning, simplified data compression, and enhanced privacy, making them a promising alternative to RGB cameras.

METHODS

Experts and novice surgeons completed two simulators of open suturing. We focused on hand and tool detection and action segmentation in suturing procedures. YOLOv8 was used for tool detection in RGB and depth videos. Furthermore, UVAST and MSTCN++ were used for action segmentation. Our study includes the collection and annotation of a dataset recorded with Azure Kinect.

RESULTS

We demonstrated that using depth cameras in object detection and action segmentation achieves comparable results to RGB cameras. Furthermore, we analyzed 3D hand path length, revealing significant differences between experts and novice surgeons, emphasizing the potential of depth cameras in capturing surgical skills. We also investigated the influence of camera angles on measurement accuracy, highlighting the advantages of 3D cameras in providing a more accurate representation of hand movements.

CONCLUSION

Our research contributes to advancing the field of surgical skill assessment by leveraging depth cameras for more reliable and privacy evaluations. The findings suggest that depth cameras can be valuable in assessing surgical skills and provide a foundation for future research in this area.

[Analysis of the challenges and dilemmas that bioethics of the 21st century will face in the digital health era]

The medical history underscores the significance of ethics in each advancement, with bioethics playing a pivotal role in addressing emerging ethical challenges in digital health (DH). This article examines the ethical dilemmas of innovations in DH, focusing on the healthcare system, professionals, and patients. Artificial Intelligence (AI) raises concerns such as confidentiality and algorithmic biases. Mobile applications (Apps) empower but pose challenges of access and digital literacy. Telemedicine (TM) democratizes and reduces healthcare costs but requires addressing the digital divide and interconsultation dilemmas; it necessitates high-quality standards with patient information protection and attention to equity in access. Wearables and the Internet of Things (IoT) transform healthcare but face ethical challenges like privacy and equity. 21st-century bioethics must be adaptable as DH tools demand constant review and consensus, necessitating health science faculties' preparedness for the forthcoming changes.

The Environmental Costs of Artificial Intelligence for Healthcare

Healthcare has emerged as a key setting where expectations are rising for the potential benefits of artificial intelligence (AI), encompassing a range of technologies of varying utility and benefit. This paper argues that, even as the development of AI for healthcare has been pushed forward by a range of public and private actors, insufficient attention has been paid to a key contradiction at the center of AI for healthcare: that its pursuit to improve health is necessarily accompanied by environmental costs which pose risks to human and environmental health-costs which are not necessarily directly borne by those benefiting from the technologies. This perspective paper begins by examining the purported promise of AI in healthcare, contrasting this with the environmental costs which arise across the AI lifecycle, to highlight this contradiction inherent in the pursuit of AI. Its advancement-including in healthcare-is often described through deterministic language that presents it as inevitable. Yet, this paper argues that there is need for recognition of the environmental harm which this pursuit can lead to. Given recent initiatives to incorporate stakeholder involvement into decision-making around AI, the paper closes with a call for an expanded conception of stakeholders in AI for healthcare, to include consideration of those who may be indirectly affected by its development and deployment.

Wearable Technologies for Detecting Burnout and Well-Being in Health Care Professionals: Scoping Review

BACKGROUND

The occupational burnout epidemic is a growing issue, and in the United States, up to 60% of medical students, residents, physicians, and registered nurses experience symptoms. Wearable technologies may provide an opportunity to predict the onset of burnout and other forms of distress using physiological markers.

OBJECTIVE

This study aims to identify physiological biomarkers of burnout, and establish what gaps are currently present in the use of wearable technologies for burnout prediction among health care professionals (HCPs).

METHODS

A comprehensive search of several databases was performed on June 7, 2022. No date limits were set for the search. The databases were Ovid: MEDLINE(R), Embase, Healthstar, APA PsycInfo, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Web of Science Core Collection via Clarivate Analytics, Scopus via Elsevier, EBSCOhost: Academic Search Premier, CINAHL with Full Text, and Business Source Premier. Studies observing anxiety, burnout, stress, and depression using a wearable device worn by an HCP were included, with HCP defined as medical students, residents, physicians, and nurses. Bias was assessed using the Newcastle Ottawa Quality Assessment Form for Cohort Studies.

RESULTS

The initial search yielded 505 papers, from which 10 (1.95%) studies were included in this review. The majority (n=9) used wrist-worn biosensors and described observational cohort studies (n=8), with a low risk of bias. While no physiological measures were reliably associated with burnout or anxiety, step count and time in bed were associated with depressive symptoms, and heart rate and heart rate variability were associated with acute stress. Studies were limited with long-term observations (eg, ≥12 months) and large sample sizes, with limited integration of wearable data with system-level information (eg, acuity) to predict burnout. Reporting standards were also insufficient, particularly in device adherence and sampling frequency used for physiological measurements.

CONCLUSIONS

With wearables offering promise for digital health assessments of human functioning, it is possible to see wearables as a frontier for predicting burnout. Future digital health studies exploring the utility of wearable technologies for burnout prediction should address the limitations of data standardization and strategies to improve adherence and inclusivity in study participation.

Challenges of artificial intelligence in medicine and dermatology

Artificial intelligence (AI) in medicine and dermatology brings additional challenges related to bias, transparency, ethics, security, and inequality. Bias in AI algorithms can arise from biased training data or decision-making processes, leading to disparities in health care outcomes. Addressing bias requires careful examination of the data used to train AI models and implementation of strategies to mitigate bias during algorithm development. Transparency is another critical challenge, as AI systems often operate as black boxes, making it difficult to understand how decisions are reached. Ensuring transparency in AI algorithms is vital to gaining trust from both patients and health care providers. Ethical considerations arise when using AI in health care, including issues such as informed consent, privacy, and the responsibility for the decisions made by AI systems. It is essential to establish clear guidelines and frameworks that govern the ethical use of AI, including maintaining patient autonomy and protecting sensitive health information. Security is a significant concern in AI systems, as they rely on vast amounts of sensitive patient data. Protecting these data from unauthorized access, breaches, or malicious attacks is paramount to maintaining patient privacy and trust in AI technologies. Lastly, the potential for inequality arises if AI technologies are not accessible to all populations, leading to a digital divide in health care. Efforts should be made to ensure that AI solutions are affordable, accessible, and tailored to the needs of diverse communities, mitigating the risk of exacerbating existing health care disparities. Addressing these challenges is crucial for AI's responsible and equitable integration in medicine and dermatology.

Digital Neuropsychology beyond Computerized Cognitive Assessment: Applications of Novel Digital Technologies

Compared with other health disciplines, there is a stagnation in technological innovation in the field of clinical neuropsychology. Traditional paper-and-pencil tests have a number of shortcomings, such as low-frequency data collection and limitations in ecological validity. While computerized cognitive assessment may help overcome some of these issues, current computerized paradigms do not address the majority of these limitations. In this paper, we review recent literature on the applications of novel digital health approaches, including ecological momentary assessment, smartphone-based assessment and sensors, wearable devices, passive driving sensors, smart homes, voice biomarkers, and electronic health record mining, in neurological populations. We describe how each digital tool may be applied to neurologic care and overcome limitations of traditional neuropsychological assessment. Ethical considerations, limitations of current research, as well as our proposed future of neuropsychological practice are also discussed.

Human Healthcare and Its Pharmacy Component from a Safety Point of View

Healthcare plays a crucial role in public and national safety as a significant part of state activity and a component of national safety, whose mission is to organize and ensure affordable medical care for the population. The four stages of the genesis of healthcare safety development with the corresponding safety models of formation were defined: technical, human factor or security management, systemic security management, and cognitive complexity. It was established that at all stages, little attention is paid to the issues of the formation of the pharmaceutical sector's safety. Taking into account the development of safety models that arise during the four stages of the genesis of safety science, we have proposed a model of the evolution of pharmaceutical safety formation. At the same time, future research is proposed to focus on new holistic concepts of safety, such as "Safety II", evaluation and validation methods, especially in the pharmaceutical sector, where the development of this topic remained in the second stage of the evolution of science, the search for pharmaceutical errors related to drugs.

The impact of nuance DAX ambient listening AI documentation: a cohort study

OBJECTIVE

To assess the impact of the use of an ambient listening/digital scribing solution (Nuance Dragon Ambient eXperience (DAX)) on caregiver engagement, time spent on Electronic Health Record (EHR) including time after hours, productivity, attributed panel size for value-based care providers, documentation timeliness, and Current Procedural Terminology (CPT) submissions.

MATERIALS AND METHODS

We performed a peer-matched controlled cohort study from March to September 2022 to evaluate the impact of DAX in outpatient clinics in an integrated healthcare system. Primary outcome measurements included provider engagement survey results, reported patient safety events related to DAX use, patients' Likelihood to Recommend score, number of patients opting out of ambient listening, change in work relative values units, attributed value-based primary care panel size, documentation completion and CPT code submission deficiency rates, and note turnaround time.

RESULTS

A total of 99 providers representing 12 specialties enrolled in the study; 76 matched control group providers were included for analysis. Median utilization of DAX was 47% among active participants. We found positive trends in provider engagement, while non-participants saw worsening engagement and no practical change in productivity. There was a statistically significant worsening of after-hours EHR. There was no quantifiable effect on patient safety.

DISCUSSION

Nuance DAX use showed positive trends in provider engagement at no risk to patient safety, experience, or clinical documentation. There were no significant benefits to patient experience, documentation, or measures of provider productivity.

CONCLUSION

Our results highlight the potential of ambient dictation as a tool for improving the provider experience. Head-to-head comparisons of EHR documentation efficiency training are needed.

Automating sedation state assessments using natural language processing

INTRODUCTION

Common goals for procedural sedation are to control pain and ensure the patient is not moving to an extent that is impeding safe progress or completion of the procedure. Clinicians perform regular assessments of the adequacy of procedural sedation in accordance with these goals to inform their decision-making around sedation titration and also for documentation of the care provided. Natural language processing could be applied to real-time transcriptions of audio recordings made during procedures in order to classify sedation states that involve movement and pain, which could then be integrated into clinical documentation systems. The aim of this study was to determine whether natural language processing algorithms will work with sufficient accuracy to detect sedation states during procedural sedation.

DESIGN

A prospective observational study was conducted.

METHODS

Audio recordings from consenting participants undergoing elective procedures performed in the interventional radiology suite at a large academic hospital were transcribed using an automated speech recognition model. Sentences of transcribed text were used to train and evaluate several different NLP pipelines for a text classification task. The NLP pipelines we evaluated included a simple Bag-of-Words (BOW) model, an ensemble architecture combining a linear BOW model and a "token-to-vector" (Tok2Vec) component, and a transformer-based architecture using the RoBERTa pre-trained model.

RESULTS

A total of 15,936 sentences from transcriptions of 82 procedures was included in the analysis. The RoBERTa model achieved the highest performance among the three models with an area under the ROC curve (AUC-ROC) of 0.97, an F1 score of 0.87, a precision of 0.86, and a recall of 0.89. The Ensemble model showed a similarly high AUC-ROC of 0.96, but lower F1 score of 0.79, precision of 0.83, and recall of 0.77. The BOW approach achieved an AUC-ROC of 0.97 and the F1 score was 0.7, precision was 0.83 and recall was 0.66.

CONCLUSION

The transformer-based architecture using the RoBERTa pre-trained model achieved the best classification performance. Further research is required to confirm the that this natural language processing pipeline can accurately perform text classifications with real-time audio data to allow for automated sedation state assessments.

CLINICAL RELEVANCE

Automating sedation state assessments using natural language processing pipelines would allow for more timely documentation of the care received by sedated patients, and, at the same time, decrease documentation burden for clinicians. Downstream applications can also be generated from the classifications, including for example real-time visualizations of sedation state, which may facilitate improved communication of the adequacy of the sedation between clinicians, who may be performing supervision remotely. Also, accumulation of sedation state assessments from multiple procedures may reveal insights into the efficacy of particular sedative medications or identify procedures where the current approach for sedation and analgesia is not optimal (i.e. a significant amount of time spent in "pain" or "movement" sedation states).

CUSCO: An Unobtrusive Custom Secure Audio-Visual Recording System for Ambient Assisted Living

The ubiquity of digital technology has facilitated detailed recording of human behaviour. Ambient technology has been used to capture behaviours in a broad range of applications ranging from healthcare and monitoring to assessment of cooperative work. However, existing systems often face challenges in terms of autonomy, usability, and privacy. This paper presents a portable, easy-to-use and privacy-preserving system for capturing behavioural signals unobtrusively in home or in office settings. The system focuses on the capture of audio, video, and depth imaging. It is based on a device built on a small-factor platform that incorporates ambient sensors which can be integrated with the audio and depth video hardware for multimodal behaviour tracking. The system can be accessed remotely and integrated into a network of sensors. Data are encrypted in real time to ensure safety and privacy. We illustrate uses of the device in two different settings, namely, a healthy-ageing IoT application, where the device is used in conjunction with a range of IoT sensors to monitor an older person's mental well-being at home, and a healthcare communication quality assessment application, where the device is used to capture a patient-clinician interaction for consultation quality appraisal. CUSCO can automatically detect active speakers, extract acoustic features, record video and depth streams, and recognise emotions and cognitive impairment with promising accuracy.

A voice-based digital assistant for intelligent prompting of evidence-based practices during ICU rounds

OBJECTIVE

To evaluate the technical feasibility and potential value of a digital assistant that prompts intensive care unit (ICU) rounding teams to use evidence-based practices based on analysis of their real-time discussions.

METHODS

We evaluated a novel voice-based digital assistant which audio records and processes the ICU care team's rounding discussions to determine which evidence-based practices are applicable to the patient but have yet to be addressed by the team. The system would then prompt the team to consider indicated but not yet delivered practices, thereby reducing cognitive burden compared to traditional rigid rounding checklists. In a retrospective analysis, we applied automatic transcription, natural language processing, and a rule-based expert system to generate personalized prompts for each patient in 106 audio-recorded ICU rounding discussions. To assess technical feasibility, we compared the system's prompts to those created by experienced critical care nurses who directly observed rounds. To assess potential value, we also compared the system's prompts to a hypothetical paper checklist containing all evidence-based practices.

RESULTS

The positive predictive value, negative predictive value, true positive rate, and true negative rate of the system's prompts were 0.45 ± 0.06, 0.83 ± 0.04, 0.68 ± 0.07, and 0.66 ± 0.04, respectively. If implemented in lieu of a paper checklist, the system would generate 56% fewer prompts per patient, with 50%±17% greater precision.

CONCLUSION

A voice-based digital assistant can reduce prompts per patient compared to traditional approaches for improving evidence uptake on ICU rounds. Additional work is needed to evaluate field performance and team acceptance.

Artificial intelligence & clinical nutrition: What the future might have in store

Artificial Intelligence (AI) is a rapidly emerging technology in healthcare that has the potential to revolutionise clinical nutrition. AI can assist in analysing complex data, interpreting medical images, and providing personalised nutrition interventions for patients. Clinical nutrition is a critical aspect of patient care, and AI can help clinicians make more informed decisions regarding patients' nutritional requirements, disease prevention, and management. AI algorithms can analyse large datasets to identify novel associations between diet and disease outcomes, enabling clinicians to make evidence-based nutritional recommendations. AI-powered devices and applications can also assist in tracking dietary intake, providing feedback, and motivating patients towards healthier food choices. However, the adoption of AI in clinical nutrition raises several ethical and regulatory concerns, such as data privacy and bias. Further research is needed to assess the clinical effectiveness and safety of AI-powered nutrition interventions. In conclusion, AI has the potential to transform clinical nutrition, but its integration into clinical practice should be carefully monitored to ensure patient safety and benefit. This article discusses the current and future applications of AI in clinical nutrition and highlights its potential benefits.

Exploring Older Adults' Willingness to Install Home Surveil-Lance Systems in Taiwan: Factors and Privacy Concerns

Taiwan has a rapidly increasing aging population with a considerably high life expectancy rate, which poses challenges for healthcare and medical systems. This study examines three key factors: safety concerns, family expectations, and privacy concerns, and their influence on surveillance system installation decisions. A cross-sectional study was conducted involving a group of physically active older adults in Taiwan, using a questionnaire to collect data on the reasons for in-stalling a surveillance system and preferences for three image privacy protection techniques: blurring the face and transformation to a 2D or 3D character. The study concluded that while safety concerns and family expectations facilitate the adoption of surveillance systems, privacy concerns serve as a significant barrier. Furthermore, older adults showed a clear preference for avatar-based privacy protection methods over simpler techniques, such as blurring. The outcomes of this research will be instrumental in shaping the development of privacy-conscious home surveillance technologies, adeptly balancing safety and privacy. This understanding can pave the way for technology design that skillfully balances privacy concerns with remote monitoring quality, thereby enhancing the well-being and safety of this demographic. These results could possibly be extended to other demographics as well.

Mapping emerging technologies in aged care: results from an in-depth online research

BACKGROUND

Emerging Technologies (ETs) have recently acquired great relevance in elderly care. The exceptional experience with SARS-CoV-2 pandemic has emphasized the usefulness of ETs in the assistance and remote monitoring of older adults. Technological devices have also contributed to the preservation of social interactions, thus reducing isolation and loneliness. The general purpose of this work is to provide a comprehensive and updated overview of the technologies currently employed in elderly care. This objective was achieved firstly, by mapping and classifying the ETs currently available on the market and, secondly, by assessing the impact of such ETs on elderly care, exploring the ethical values promoted, as well as potential ethical threats.

METHODS

An in-depth search was carried out on Google search engine, by using specific keywords (e.g. technology, monitoring techniques, ambient intelligence; elderly, older adults; care and assistance). Three hundred and twenty-eight technologies were originally identified. Then, based on a predetermined set of inclusion-exclusion criteria, two hundreds and twenty-two technologies were selected.

RESULTS

A comprehensive database was elaborated, where the two hundred and twenty-two ETs selected were classified as follows: category; developmental stage; companies and/or partners; functions; location of development; time of development; impact on elderly care; target; website. From an in-depth qualitative analysis, some ethically-related contents and themes emerged, namely: questions related to safety, independence and active aging, connectedness, empowerment and dignity, cost and efficiency. Although not reported by developers, a close analysis of website contents highlights that positive values are often associated with potential risks, notably privacy threats, deception, dehumanization of care.

CONCLUSIONS

Research findings may ultimately lead to a better understanding of the impact of ETs on elderly people.

Geriatric Care Management System Powered by the IoT and Computer Vision Techniques

The digitalisation of geriatric care refers to the use of emerging technologies to manage and provide person-centered care to the elderly by collecting patients' data electronically and using them to streamline the care process, which improves the overall quality, accuracy, and efficiency of healthcare. In many countries, healthcare providers still rely on the manual measurement of bioparameters, inconsistent monitoring, and paper-based care plans to manage and deliver care to elderly patients. This can lead to a number of problems, including incomplete and inaccurate record-keeping, errors, and delays in identifying and resolving health problems. The purpose of this study is to develop a geriatric care management system that combines signals from various wearable sensors, noncontact measurement devices, and image recognition techniques to monitor and detect changes in the health status of a person. The system relies on deep learning algorithms and the Internet of Things (IoT) to identify the patient and their six most pertinent poses. In addition, the algorithm has been developed to monitor changes in the patient's position over a longer period of time, which could be important for detecting health problems in a timely manner and taking appropriate measures. Finally, based on expert knowledge and a priori rules integrated in a decision tree-based model, the automated final decision on the status of nursing care plan is generated to support nursing staff.

The ethical matrix as a method for involving people living with disease and the wider public (PPI) in near-term artificial intelligence research

INTRODUCTION

The rapid pace of research in the field of Artificial Intelligence in medicine has associated risks for near-term AI. Ethical considerations of the use of AI in medicine remain a subject of much debate. Concurrently, the Involvement of People living with disease and the Public (PPI) in research is becoming mandatory in the EU and UK. The goal of this research was to elucidate the important values for our relevant stakeholders: People with MS, Radiologists, neurologists, Registered Healthcare Practitioners and Computer Scientists concerning AI in radiology and synthesize these in an ethical matrix.

METHODS

An ethical matrix workshop co-designed with a patient expert. The workshop yielded a survey which was disseminated to the professional societies of the relevant stakeholders. Quantitative data were analysed using the Pingouin 0.53 python package. Qualitative data were examined with word frequency analysis and analysed for themes with grounded theory with a patient expert.

RESULTS

184 participants were recruited, (54, 60, 17, 12, 41 respectively). There were significant (p < 0.00001) differences in age, gender and ethnicity between groups. Key themes emerging from our results were the importance fast and accurate results, explanations over model performance and the significance of maintaining personal connections and choice. These themes were used to construct the ethical matrix.

CONCLUSION

The ethical matrix is a useful tool for PPI and stakeholder engagement with particular advantages for near-term AI in the pandemic era.

IMPLICATIONS FOR PRACTICE

We have produced an ethical matrix that allows for the inclusion of stakeholder opinion in medical AI research design.

Trojan technology in the living room?

Definition of the problem

Assistive technologies, including “smart” instruments and artificial intelligence (AI), are increasingly arriving in older adults’ living spaces. Various research has explored risks (“surveillance technology”) and potentials (“independent living”) to people’s self-determination from technology itself and from the increasing complexity of sociotechnical interactions. However, the point at which self-determination of the individual is overridden by external influences has not yet been sufficiently studied. This article aims to shed light on this point of transition and its implications.

Arguments

The identification of this “tipping point” could contribute to analysis of familiar issues of conflict between the ethical principles of beneficence and respect for autonomy. When using technology in the living spaces of older adults, relationships, among other factors, may play a crucial role in older adult’s self-determination. We find the tipping point to occur subjectively and variably. To this end, the article combines theoretical ethical considerations with two examples from a qualitative study illustrating the perspective of older adults aged 65–85 years.

Conclusion

The study of the tipping point underscores the importance of perceiving an older person’s perspective. Particularly at the tipping point, this might be the only way to effectively identify whether the individual concerned perceives their self-determination as externally overridden. In conceptualizing the tipping point itself as well as its variability, we might create the basis for a normative call to shift the tipping point to promote self-determination and prevent overriding the will of older adults. We highlight individual, relational, and societal implications of our findings.

Predicting gene mutation status via artificial intelligence technologies based on multimodal integration (MMI) to advance precision oncology

Personalized treatment strategies for cancer frequently rely on the detection of genetic alterations which are determined by molecular biology assays. Historically, these processes typically required single-gene sequencing, next-generation sequencing, or visual inspection of histopathology slides by experienced pathologists in a clinical context. In the past decade, advances in artificial intelligence (AI) technologies have demonstrated remarkable potential in assisting physicians with accurate diagnosis of oncology image-recognition tasks. Meanwhile, AI techniques make it possible to integrate multimodal data such as radiology, histology, and genomics, providing critical guidance for the stratification of patients in the context of precision therapy. Given that the mutation detection is unaffordable and time-consuming for a considerable number of patients, predicting gene mutations based on routine clinical radiological scans or whole-slide images of tissue with AI-based methods has become a hot issue in actual clinical practice. In this review, we synthesized the general framework of multimodal integration (MMI) for molecular intelligent diagnostics beyond standard techniques. Then we summarized the emerging applications of AI in the prediction of mutational and molecular profiles of common cancers (lung, brain, breast, and other tumor types) pertaining to radiology and histology imaging. Furthermore, we concluded that there truly exist multiple challenges of AI techniques in the way of its real-world application in the medical field, including data curation, feature fusion, model interpretability, and practice regulations. Despite these challenges, we still prospect the clinical implementation of AI as a highly potential decision-support tool to aid oncologists in future cancer treatment management.

Passive monitoring by smart toilets for precision health

Smart toilets are a key tool for enabling precision health monitoring in the home, but such passive monitoring has ethical considerations.

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.

Breaking bad news in the era of artificial intelligence and algorithmic medicine: an exploration of disclosure and its ethical justification using the hedonic calculus

An appropriate ethical framework around the use of Artificial Intelligence (AI) in healthcare has become a key desirable with the increasingly widespread deployment of this technology. Advances in AI hold the promise of improving the precision of outcome prediction at the level of the individual. However, the addition of these technologies to patient-clinician interactions, as with any complex human interaction, has potential pitfalls. While physicians have always had to carefully consider the ethical background and implications of their actions, detailed deliberations around fast-moving technological progress may not have kept up. We use a common but key challenge in healthcare interactions, the disclosure of bad news (likely imminent death), to illustrate how the philosophical framework of the 'Felicific Calculus' developed in the eighteenth century by Jeremy Bentham, may have a timely quasi-quantitative application in the age of AI. We show how this ethical algorithm can be used to assess, across seven mutually exclusive and exhaustive domains, whether an AI-supported action can be morally justified.

Application of Internet of Things and Sensors in Healthcare

The Internet of Things (IoT) is an innovative technology with billions of sensors in various IoT applications. Important elements used in the IoT are sensors that collect data for desired analyses. The IoT and sensors are very important in smart cities, smart agriculture, smart education, healthcare systems, and other applications. The healthcare system uses the IoT to meet global health challenges, and the newest example is COVID-19. Demand has increased during COVID-19 for healthcare to reach patients remotely and digitally at their homes. The IoT properly monitors patients using an interconnected network to overcome the issues of healthcare services. The aim of this paper is to discuss different applications, technologies, and challenges related to the healthcare system. Different databases were searched using keywords in Google Scholar, Elsevier, PubMed, ACM, ResearchGate, Scopus, Springer, etc. This paper discusses, highlights, and identifies the applications of IoT healthcare systems to provide research directions to healthcare, academia, and researchers to overcome healthcare system challenges. Hence, the IoT can be beneficial by providing better treatments using the healthcare system efficiently. In this paper, the integration of the IoT with smart technologies not only improves computation, but will also allow the IoT to be pervasive, profitable, and available anytime and anywhere. Finally, some future directions and challenges are discussed, along with useful suggestions that can assist the IoT healthcare system during COVID-19 and in a severe pandemic.

Assessing Pain Research: A Narrative Review of Emerging Pain Methods, Their Technosocial Implications, and Opportunities for Multidisciplinary Approaches

Pain research traverses many disciplines and methodologies. Yet, despite our understanding and field-wide acceptance of the multifactorial essence of pain as a sensory perception, emotional experience, and biopsychosocial condition, pain scientists and practitioners often remain siloed within their domain expertise and associated techniques. The context in which the field finds itself today-with increasing reliance on digital technologies, an on-going pandemic, and continued disparities in pain care-requires new collaborations and different approaches to measuring pain. Here, we review the state-of-the-art in human pain research, summarizing emerging practices and cutting-edge techniques across multiple methods and technologies. For each, we outline foreseeable technosocial considerations, reflecting on implications for standards of care, pain management, research, and societal impact. Through overviewing alternative data sources and varied ways of measuring pain and by reflecting on the concerns, limitations, and challenges facing the field, we hope to create critical dialogues, inspire more collaborations, and foster new ideas for future pain research methods.

An Idealized Clinicogenomic Registry to Engage Underrepresented Populations Using Innovative Technology

Current best practices in tumor registries provide a glimpse into a limited time frame over the natural history of disease, usually a narrow window around diagnosis and biopsy. This creates challenges meeting public health and healthcare reimbursement policies that increasingly require robust documentation of long-term clinical trajectories, quality of life, and health economics outcomes. These challenges are amplified for underrepresented minority (URM) and other disadvantaged populations, who tend to view the institution of clinical research with skepticism. Participation gaps leave such populations underrepresented in clinical research and, importantly, in policy decisions about treatment choices and reimbursement, thus further augmenting health, social, and economic disparities. Cloud computing, mobile computing, digital ledgers, tokenization, and artificial intelligence technologies are powerful tools that promise to enhance longitudinal patient engagement across the natural history of disease. These tools also promise to enhance engagement by giving participants agency over their data and addressing a major impediment to research participation. This will only occur if these tools are available for use with all patients. Distributed ledger technologies (specifically blockchain) converge these tools and offer a significant element of trust that can be used to engage URM populations more substantively in clinical research. This is a crucial step toward linking composite cohorts for training and optimization of the artificial intelligence tools for enhancing public health in the future. The parameters of an idealized clinical genomic registry are presented.

Developing, implementing and governing artificial intelligence in medicine: a step-by-step approach to prevent an artificial intelligence winter

Objective

Although the role of artificial intelligence (AI) in medicine is increasingly studied, most patients do not benefit because the majority of AI models remain in the testing and prototyping environment. The development and implementation trajectory of clinical AI models are complex and a structured overview is missing. We therefore propose a step-by-step overview to enhance clinicians’ understanding and to promote quality of medical AI research.

Methods

We summarised key elements (such as current guidelines, challenges, regulatory documents and good practices) that are needed to develop and safely implement AI in medicine.

Conclusion

This overview complements other frameworks in a way that it is accessible to stakeholders without prior AI knowledge and as such provides a step-by-step approach incorporating all the key elements and current guidelines that are essential for implementation, and can thereby help to move AI from bytes to bedside.

Management of Medico-Legal Risks in Digital Health Era: A Scoping Review

Artificial intelligence needs big data to develop reliable predictions. Therefore, storing and processing health data is essential for the new diagnostic and decisional technologies but, at the same time, represents a risk for privacy protection. This scoping review is aimed at underlying the medico-legal and ethical implications of the main artificial intelligence applications to healthcare, also focusing on the issues of the COVID-19 era. Starting from a summary of the United States (US) and European Union (EU) regulatory frameworks, the current medico-legal and ethical challenges are discussed in general terms before focusing on the specific issues regarding informed consent, medical malpractice/cognitive biases, automation and interconnectedness of medical devices, diagnostic algorithms and telemedicine. We aim at underlying that education of physicians on the management of this (new) kind of clinical risks can enhance compliance with regulations and avoid legal risks for the healthcare professionals and institutions.

Reevaluating medication adherence in the era of digital health

INTRODUCTION

Medication adherence is a worldwide issue impacting more than half the population. The cost associated with nonadherence is tremendous and has spurred the growth of novel technologies to address this growing problem.

AREAS COVERED

This perspective covers the different digital health medication adherence tools that have come to market in the past decade and their clinical impact. These digital interventions and their applicability to medication adherence across different stakeholders are then evaluated.

EXPERT OPINION

Digital health will play a significant role in creating new pathways to care in the 2020s. However, the current design of medication adherence tools has not demonstrated a clinical impact that will be relevant for the digital health space without a change in redesign factoring in relevant stakeholders' incentives to address adherence issues. A focus on only adherence has not yielded the economic or clinical benefit as expected, which is likely due to a lack of focus on broader drug-related problems (DRPs) that are causative factors beyond adherence alone. As such, adherence tools will see disparate uptake, likely due to condition-specific interventions rather than adherence issues as a whole, and future endeavors will need to address the larger DRP considerations to actualize clinical outcomes.

Evaluation and Management of Sleep and Circadian Rhythm Disturbance in Cancer

OPINION STATEMENT

Sleep and circadian rhythm disturbance are among the most commonly experienced symptoms in patients with cancer. These disturbances occur throughout the spectrum of cancer care from diagnosis, treatment, and long into survivorship. The pathogenesis of these symptoms and disturbances is based on common inflammatory pathways related to cancer and its' treatments. The evaluation of sleep and circadian disorders requires an understanding of how these symptoms cluster with other cancer-related symptoms and potentiate each other. A thorough evaluation of these symptoms and disorders utilizing validated diagnostic tools, directed review of clinical information, and diagnostic testing is recommended. Treatment of sleep and circadian disturbance in cancer patients should be based on the findings of a detailed evaluation, including specific treatment of primary sleep and circadian disorders, and utilize integrative and personalised management of cancer-related symptoms through multiple pharmacologic and non-pharmacologic modalities. Recognition, evaluation, and treatment of sleep and circadian rhythm disturbance in cancer may lead to improved symptom management, quality of life, and outcomes.

Is There a Place for Responsible Artificial Intelligence in Pandemics? A Tale of Two Countries

This research examines the considerations of responsible Artificial Intelligence in the deployment of AI-based COVID-19 digital proximity tracking and tracing applications in two countries; the State of Qatar and the United Kingdom. Based on the alignment level analysis with the Good AI Society's framework and sentiment analysis of official tweets, the diagnostic analysis resulted in contrastive findings for the two applications. While the application EHTERAZ (Arabic for precaution) in Qatar has fallen short in adhering to the responsible AI requirements, it has contributed significantly to controlling the pandemic. On the other hand, the UK's NHS COVID-19 application has exhibited limited success in fighting the virus despite relatively abiding by these requirements. This underlines the need for obtaining a practical and contextual view for a comprehensive discourse on responsible AI in healthcare. Thereby offering necessary guidance for striking a balance between responsible AI requirements and managing pressures towards fighting the pandemic.

Healthcare Professionals' Perspective on Implementing a Detector of Behavioural Disturbances in Long-Term Care Homes

The number of Canadians with dementia is expected to rise to 674,000 in the years to come. Finding ways to monitor behavioural disturbance in patients with dementia (PwDs) is crucial. PwDs can unintentionally behave in ways that are harmful to them and the people around them, such as other residents or care providers. Current practice does not involve technology to monitor PwD behaviours. Events are reported randomly by nonstaff members or when a staff member notices the absence of a PwD from a scheduled event. This study aims to explore the potential of implementing a novel detector of behavioural disturbances (DBD) in long-term care homes by mapping the perceptions of healthcare professionals and family members about this technology. Qualitative information was gathered from a focus group involving eight healthcare professionals working in a tertiary care facility and a partner of a resident admitted in the same facility. Thematic analysis resulted in three themes: (A) the ability of the DBD to detect relevant dementia-related behavioural disturbances that are typical of PwD; (B) the characteristics of the DBD and clinical needs and preferences; (C) the integration of the DBD into daily routines. The results tend to confirm the adequacy of the DBD to the day-to-day needs for the detection of behavioural disturbances and hazardous behaviours. The DBD was considered to be useful and easy to use in the tertiary care facility examined in this study. The participants intend to use the DBD in the future, which means that it has a high degree of acceptance.