Going beyond the means: Exploring the role of bias from digital determinants of health in technologies

A new era for stress research: supporting user performance and experience in the digital age

Stress is both a driver of objective performance impairments and a source of negative user experience of technology. This review addresses future directions for research on stress and ergonomics in the digital age. The review is structured around three levels of analysis. At the individual user level, stress is elicited by novel technologies and tasks including interaction with AI and robots, working in Virtual Reality, and operating autonomous vehicles. At the organisational level, novel, potentially stressful challenges include maintaining cybersecurity, surveillance and monitoring of employees supported by technology, and addressing bias and discrimination in the workplace. At the sociocultural level, technology, values and norms are evolving symbiotically, raising novel demands illustrated with respect to interactions with social media and new ethical challenges. We also briefly review the promise of neuroergonomics and emotional design to support stress mitigation. We conclude with seven high-level principles that may guide future work.

Demographic reporting in biosignal datasets: a comprehensive analysis of the PhysioNet open access database

The PhysioNet open access database (PND) is one of the world's largest and most comprehensive repositories of biosignal data and is widely used by researchers to develop, train, and validate algorithms. To contextualise the results of such algorithms, understanding the underlying demographic distribution of the data is crucial-specifically, the race, ethnicity, sex or gender, and age of study participants. We sought to understand the underlying reporting patterns and characteristics of the demographic data of the datasets available on PND. Of the 181 unique datasets present in the PND as of July 6, 2023, 175 involved human participants, with less than 7% of studies reporting on all four of the key demographic variables. Furthermore, we found a higher rate of reporting sex or gender and age than race and ethnicity. In the studies that did include participant sex or gender, the samples were mostly male. Additionally, we found that most studies were done in North America, particularly in the USA. These imbalances and poor reporting of representation raise concerns regarding potential embedded biases in the algorithms that rely on these datasets. They also underscore the need for universal and comprehensive reporting practices to ensure equitable development and deployment of artificial intelligence and machine learning tools in medicine.

A Clinician's Guide to Understanding Bias in Critical Clinical Prediction Models

This narrative review focuses on the role of clinical prediction models in supporting informed decision-making in critical care, emphasizing their 2 forms: traditional scores and artificial intelligence (AI)-based models. Acknowledging the potential for both types to embed biases, the authors underscore the importance of critical appraisal to increase our trust in models. The authors outline recommendations and critical care examples to manage risk of bias in AI models. The authors advocate for enhanced interdisciplinary training for clinicians, who are encouraged to explore various resources (books, journals, news Web sites, and social media) and events (Datathons) to deepen their understanding of risk of bias.

Telemedicine in Cancer Rehabilitation: Applications and Opportunities Across the Cancer Care Continuum

ABSTRACT

Advancements in telemedicine have revolutionized the landscape of healthcare delivery, with particular implications for cancer rehabilitation. This journal article provides a comprehensive review of the utilization and application of telemedicine in cancer rehabilitation, spanning the entire cancer care continuum. The integration of telemedicine in cancer rehabilitation services is explored from diagnosis through survivorship, addressing the unique challenges and opportunities at each stage.