Machine Learning and Artificial Intelligence (AI) more broadly have great immediate and future potential for transforming almost all aspects of medicine. However, in many applications, even outside medicine, a lack of transparency in AI applications has become increasingly problematic. This is particularly pronounced where users need to interpret the output of AI systems. Explainable AI (XAI) provides a rationale that allows users to understand why a system has produced a given output. The output can then be interpreted within a given context. One area that is in great need of XAI is that of Clinical Decision Support Systems (CDSSs). These systems support medical practitioners in their clinic decision-making and in the absence of explainability may lead to issues of under or over-reliance. Providing explanations for how recommendations are arrived at will allow practitioners to make more nuanced, and in some cases, life-saving decisions. The need for XAI in CDSS, and the medical field in general, is amplified by the need for ethical and fair decision-making and the fact that AI trained with historical data can be a reinforcement agent of historical actions and biases that should be uncovered. We performed a systematic literature review of work to-date in the application of XAI in CDSS. Tabular data processing XAI-enabled systems are the most common, while XAI-enabled CDSS for text analysis are the least common in literature. There is more interest in developers for the provision of local explanations, while there was almost a balance between post-hoc and ante-hoc explanations, as well as between model-specific and model-agnostic techniques. Studies reported benefits of the use of XAI such as the fact that it could enhance decision confidence for clinicians, or generate the hypothesis about causality, which ultimately leads to increased trustworthiness and acceptability of the system and potential for its incorporation in the clinical workflow. However, we found an overall distinct lack of application of XAI in the context of CDSS and, in particular, a lack of user studies exploring the needs of clinicians. We propose some guidelines for the implementation of XAI in CDSS and explore some opportunities, challenges, and future research needs.

Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review

South Asia faces one of the largest gender gaps online globally, and online safety is one of the main barriers to gender-equitable Internet access [GSMA, 2015]. To better understand the gendered risks and coping practices online in South Asia, we present a qualitative study of the online abuse experiences and coping practices of 199 people who identified as women and 6 NGO staff from India, Pakistan, and Bangladesh, using a feminist analysis. We found that a majority of our participants regularly contended with online abuse, experiencing three major abuse types: cyberstalking, impersonation, and personal content leakages. Consequences of abuse included emotional harm, reputation damage, and physical and sexual violence. Participants coped through informal channels rather than through technological protections or law enforcement. Altogether, our findings point to opportunities for designs, policies, and algorithms to improve women's safety online in South Asia.

https://elie.net/static/files/they-dont-leave-us-alone-anywhere-we-go-gender-and-digital-abuse-in-south-asia/they-dont-leave-us-alone-anywhere-we-go-gender-and-digital-abuse-in-south-asia-paper.pdf

"They Don't Leave Us Alone Anywhere We Go": Gender and Digital Abuse in South Asia

PURPOSE This review aims to examine the instruments, approaches, scales, or assessment tools used to evaluate technology acceptance, technology adoption, and usability of information and communication technologies (ICTs) for people living with dementia and their care partners. METHODS A systematic literature review was conducted. Studies that explored the use of instruments, approaches, scales, or assessment tools to evaluate the technology acceptance and usability of ICTs for people living with dementia and their care partners were identified through five databases: Medline, EMBASE, CINAHL, Web of Science, and Scopus. RESULTS We included 74 out of 2182 papers. The most common scales used included the System Usability Scale (SUS) (11%), the ISONORM 9241/10 Questionnaire (4%), and the Post-Study System Usability Questionnaire (PSSUQ) (4%). Most (59%) of the included approaches, however, were bespoke (i.e., created by the authors for a particular study) and were not named. The approaches or tools used to assess technology acceptance, technology adoption, and usability of ICTs that applied to people living with dementia had an average of 15 items and used an average of 5.23 scale points. CONCLUSION There is no clear, standardised approach for assessing the technology acceptance, technology adoption, and usability of ICTs for people living with dementia and their care partners. The findings of this review may be used by academics to design and implement improved and more consistent assessment tools to assess technology acceptance, technology adoption, and usability of ICTs for people living with dementia and their care partners.IMPLICATIONS FOR REHABILITATIONThe number of ICTs for people with dementia and their care partners that can be used for rehabilitation is increasingThe most commonly recognized assessment tools used in this study were the SUS, ISONORM 9241/10, and PSSUQ questionnaires.For the custom assessment tools, the average number of items included in this study was 15 with five-point bidirectional labelling.There is no clear, standardized approach for assessing the technology acceptance, technology adoption, or usability of ICTs for people with dementia and their care partners.

Acceptance, adoption, and usability of information and communication technologies for people living with dementia and their care partners: a systematic review.

Fitness sensors and health systems are paving the way toward improving the quality of medical care by exploiting the benefits of new technology. For example, the great amount of patient-generated health data available today gives new opportunities to measure life parameters in real time and create a revolution in communication for professionals and patients. In this work, we concentrated on the basic parameter typically measured by fitness applications and devices-the number of steps taken daily. In particular, the main goal of this study was to compare the accuracy and precision of smartphone applications versus those of wearable devices to give users an idea about what can be expected regarding the relative difference in measurements achieved using different system typologies. In particular, the data obtained showed a difference of approximately 30%, proving that smartphone applications provide inaccurate measurements in long-term analysis, while wearable devices are precise and accurate. Accordingly, we challenge the reliability of previous studies reporting data collected with phone-based applications, and besides discussing the current limitations, we support the use of wearable devices for mHealth.

https://www.mdpi.com/1424-8220/20/21/6293/pdf

Accuracy of Mobile Applications versus Wearable Devices in Long-Term Step Measurements.

The growing desire for better control of health outcomes and the increasing healthcare costs associated with disease treatment has led to a shift in the healthcare paradigm from reactive to proactive. Advances in artificial intelligence (AI), the study of intelligent machines that maximize their likelihood of achieving a goal, and the rise of mobile health technologies (e.g., wearable devices and smartphone applications) have enabled healthcare to take place outside of the traditional clinical setting. In this chapter, we detail how AI algorithms can improve wellness assessment, aid in personalizing intervention strategies to promote healthier lifestyle behaviors, and uncover previously unknown disease risk factors. Organized across three dimensions of wellness (physical, mental, and social), this chapter highlights studies that utilize AI to incorporate new data sources or reinterpret preexisting data sources to further advance preventative medicine.

Roles of artificial intelligence in wellness, healthy living, and healthy status sensing

Sleep quality prediction in caregivers using physiological signals.

This exploratory qualitative study examined the feasibility of utilizing gaming technology that will ultimately assess task performance and stress among caregivers of dementia patients. The long-term goal is to use this unobtrusive application (app) to detect caregiver burnout for the purposes of early intervention. This preliminary study examined participant interface with a specific gaming technology called CAST (Caregiver Assessment Using Serious Gaming Technology). Ten dementia caregivers participated. Participants attended a demonstration and then interacted with a preliminary version of the CAST tablet application. Social work researchers interviewed participants using open-ended questions to gauge interest, technology skill level, and comfort with the app. Participants expressed interest and identified potential ways to further develop the system in order to increase ease of use, decrease time commitment, and improve suitability for daily use. The provided feedback will be used to refine th...

A Preliminary Qualitative Analysis on the Feasibility of Using Gaming Technology in Caregiver Assessment

This article describes the lived experiences of domestic violence victims among a convenience sample of 21 low-income Indian women. The experiences of abuse are drawn from in-depth, face-to-face interviews conducted in Mumbai, India. The qualitative analysis describes four major categories of their lived experiences: (a) types of abuse, (b) family involvement in abuse, (c) treatment of children, and (d) abandonment. Domestic violence in Indian culture includes violence from the husband as well as the in-laws. Women are expected to endure violence for fear of bringing shame to their families. Social and financial support for abused woman is lacking.

/pdf/lived-experiences-of-women-facing-domestic-violence-in-india-3k0yza7qwq.pdf

Lived Experiences of Women Facing Domestic Violence in India

As pre-diagnostic technologies are becoming increasingly accessible, using them to improve the quality of care available to dementia patients and their caregivers is of increasing interest. Specifically, we aim to develop a tool for non-invasively assessing task performance in a simple gaming application. To address this, we have developed Caregiver Assessment using Smart Technology (CAST), a mobile application that personalizes a traditional word scramble game. Its core functionality uses a Fuzzy Inference System (FIS) optimized via a Genetic Algorithm (GA) to provide customized performance measures for each user of the system. With CAST, we match the relative level of difficulty of play using the individual’s ability to solve the word scramble tasks. We provide an analysis of the preliminary results for determining task difficulty, with respect to our current participant cohort.

Caregiver Assessment Using Smart Gaming Technology: A Feasibility Study

Educational debt is on the rise, and social work salaries remain low compared with salaries of other similarly educated and trained professionals. To better understand the implications of educational debt for social workers, an online survey was sent to social workers in Ohio. More than 700 respondents provided information concerning educational debt and social work wages. It was found that educational attainment levels were correlated with educational debt burden but did not affect the length of the payback period. In contrast, type of practice setting did not affect educational debt burden but did affect payback period. Regarding social work wages, educational levels affected salaries; specifically, more time spent earning a degree resulted in higher starting and current salaries. Those with an MSW earned more than those with only a bachelor's degree in the field, both in starting and current salaries. Practice setting did not have an impact on starting salary but did affect current salary. This study has implications for social work education and advocacy work related to student debt forgiveness.

Jennifer C. Hughes

Papers

Sleep quality prediction in caregivers using physiological signals.

A Preliminary Qualitative Analysis on the Feasibility of Using Gaming Technology in Caregiver Assessment

Lived Experiences of Women Facing Domestic Violence in India

Caregiver Assessment Using Smart Gaming Technology: A Feasibility Study

Social Work Educational Debt and Salary Survey: A Snapshot from Ohio