Evaluation framework for conversational agents with artificial intelligence in health interventions: a systematic scoping review

Encouraging arm use in stroke survivors: the impact of smart reminders during a home-based intervention

BACKGROUND

Upper limb impairment post-stroke often leads to a predominant use of the less affected arm and consequent learned disuse of the affected side, hindering upper limb outcome. Wearable sensors such as accelerometers, combined with smart reminders (i.e., based on the amount of arm activity), offer a potential approach to promote increased use of the affected arm to improve upper limb use during daily life. This study aimed to evaluate the efficacy of wrist vibratory reminders during a six-week home-based intervention in chronic stroke survivors.

METHODS

We evaluated the impact of the home-based intervention on the primary outcome, the Motor Activity Log-14 Item Version scores Amount of Use (MAL-14 AOU), and the secondary outcomes MAL-14 Quality of Movement (QOM) and sensor-derived activity metrics from the affected arm. A randomized controlled trial design was used for the study: the intervention group received personalized reminders based on individualized arm activity goals, while the control group did not receive any feedback. Mixed linear models assessed the influence of the group, week of the intervention period, and initial impairment level on MAL-14 and arm activity metrics.

RESULTS

Forty-two participants were enrolled in the study. Overall, participants exhibited modest but not clinically relevant increases in MAL-14 AOU (+ 0.2 points) and QOM (+ 0.2 points) after the intervention period, with no statistically significant differences between the intervention and control group. Feasibility challenges were noted, such as adherence to wearing the trackers and sensor data quality. However, in participants with sufficiently available sensor data (n = 23), the affected arm use extracted from the sensor data was significantly higher in the intervention group (p < 0.05). The initial impairment level strongly influenced affected arm use and both MAL-14 AOU and QOM (p < 0.01).

CONCLUSIONS

The study investigated the effectiveness of incorporating activity trackers with smart reminders to increase affected arm activity among stroke survivors during daily life. While the results regarding the increased arm use at home are promising, patient-reported outcomes remained below clinically meaningful thresholds and showed no group differences. Further, it is essential to acknowledge feasibility issues such as adherence to wearing the trackers during the intervention and missing sensor data.

TRIAL REGISTRATION

NCT03294187.

Nurses' perspectives on privacy and ethical concerns regarding artificial intelligence adoption in healthcare

Background

With the increasing integration of artificial intelligence (AI) technologies into healthcare systems, there is a growing emphasis on privacy and ethical considerations. Nurses, as frontline healthcare professionals, are pivotal in-patient care and offer valuable insights into the ethical implications of AI adoption.

Objectives

This study aimed to explore nurses' perspectives on privacy and ethical concerns associated with the implementation of AI in healthcare settings.

Methods

We employed Van Manen's hermeneutic phenomenology as the qualitative research approach. Data were collected through purposive sampling from the December 7, 2023 to the January 15, 2024, with interviews conducted in Bengali. Thematic analysis was utilized following member checking and an audit trail.

Results

Six themes emerged from the research findings: Ethical dimensions of AI integration, highlighting complexities in incorporating AI ethically; Privacy challenges in healthcare AI, revealing concerns about data security and confidentiality; Balancing innovation and ethical practice, indicating a need to reconcile technological advancements with ethical considerations; Human touch vs. technological progress, underscoring tensions between automation and personalized care; Patient-centered care in the AI era, emphasizing the importance of maintaining focus on patients amidst technological advancements; and Ethical preparedness and education, suggesting a need for enhanced training and education on ethical AI use in healthcare.

Conclusions

The findings underscore the importance of addressing privacy and ethical concerns in AI healthcare development. Nurses advocate for patient-centered approaches and collaborate with policymakers and tech developers to ensure responsible AI adoption. Further research is imperative for mitigating ethical challenges and promoting ethical AI in healthcare practice.

Redefining Virtual Assistants in Health Care: The Future With Large Language Models

This editorial explores the evolving and transformative role of large language models (LLMs) in enhancing the capabilities of virtual assistants (VAs) in the health care domain, highlighting recent research on the performance of VAs and LLMs in health care information sharing. Focusing on recent research, this editorial unveils the marked improvement in the accuracy and clinical relevance of responses from LLMs, such as GPT-4, compared to current VAs, especially in addressing complex health care inquiries, like those related to postpartum depression. The improved accuracy and clinical relevance with LLMs mark a paradigm shift in digital health tools and VAs. Furthermore, such LLM applications have the potential to dynamically adapt and be integrated into existing VA platforms, offering cost-effective, scalable, and inclusive solutions. These suggest a significant increase in the applicable range of VA applications, as well as the increased value, risk, and impact in health care, moving toward more personalized digital health ecosystems. However, alongside these advancements, it is necessary to develop and adhere to ethical guidelines, regulatory frameworks, governance principles, and privacy and safety measures. We need a robust interdisciplinary collaboration to navigate the complexities of safely and effectively integrating LLMs into health care applications, ensuring that these emerging technologies align with the diverse needs and ethical considerations of the health care domain.