Artificial intelligence in communication impacts language and social relationships.

Artificial intelligence (AI) is already widely used in daily communication, but despite concerns about AI's negative effects on society the social consequences of using it to communicate remain largely unexplored. We investigate the social consequences of one of the most pervasive AI applications, algorithmic response suggestions ("smart replies"), which are used to send billions of messages each day. Two randomized experiments provide evidence that these types of algorithmic recommender systems change how people interact with and perceive one another in both pro-social and anti-social ways. We find that using algorithmic responses changes language and social relationships. More specifically, it increases communication speed, use of positive emotional language, and conversation partners evaluate each other as closer and more cooperative. However, consistent with common assumptions about the adverse effects of AI, people are evaluated more negatively if they are suspected to be using algorithmic responses. Thus, even though AI can increase the speed of communication and improve interpersonal perceptions, the prevailing anti-social connotations of AI undermine these potential benefits if used overtly.

Supporting Accurate Interpretation of Self-Administered Medical Test Results for Mobile Health: Assessment of Design, Demographics, and Health Condition.

BACKGROUND: Technological advances in personal informatics allow people to track their own health in a variety of ways, representing a dramatic change in individuals' control of their own wellness. However, research regarding patient interpretation of traditional medical tests highlights the risks in making complex medical data available to a general audience. OBJECTIVE: This study aimed to explore how people interpret medical test results, examined in the context of a mobile blood testing system developed to enable self-care and health management. METHODS: In a preliminary investigation and main study, we presented 27 and 303 adults, respectively, with hypothetical results from several blood tests via one of the several mobile interface designs: a number representing the raw measurement of the tested biomarker, natural language text indicating whether the biomarker's level was low or high, or a one-dimensional chart illustrating this level along a low-healthy axis. We measured respondents' correctness in evaluating these results and their confidence in their interpretations. Participants also told us about any follow-up actions they would take based on the result and how they envisioned, generally, using our proposed personal health system. RESULTS: We find that a majority of participants (242/328, 73.8%) were accurate in their interpretations of their diagnostic results. However, 135 of 328 participants (41.1%) expressed uncertainty and confusion about their ability to correctly interpret these results. We also find that demographics and interface design can impact interpretation accuracy, including false confidence, which we define as a respondent having above average confidence despite interpreting a result inaccurately. Specifically, participants who saw a natural language design were the least likely (421.47 times, P=.02) to exhibit false confidence, and women who saw a graph design were less likely (8.67 times, P=.04) to have false confidence. On the other hand, false confidence was more likely among participants who self-identified as Asian (25.30 times, P=.02), white (13.99 times, P=.01), and Hispanic (6.19 times, P=.04). Finally, with the natural language design, participants who were more educated were, for each one-unit increase in education level, more likely (3.06 times, P=.02) to have false confidence. CONCLUSIONS: Our findings illustrate both promises and challenges of interpreting medical data outside of a clinical setting and suggest instances where personal informatics may be inappropriate. In surfacing these tensions, we outline concrete interface design strategies that are more sensitive to users' capabilities and conditions.

Enhancing the Usability of an Optical Reader System to Support Point-of-Care Rapid Diagnostic Testing: An Iterative Design Approach.

BACKGROUND: In today's health care environment, increasing costs and inadequate medical resources have created a worldwide need for more affordable diagnostic tools that are also portable, fast, and easy to use. To address this issue, numerous research and commercial efforts have focused on developing rapid diagnostic technologies; however, the efficacy of existing systems has been hindered by usability problems or high production costs, making them infeasible for deployment in at-home, point-of-care (POC), or resource-limited settings. OBJECTIVE: The aim of this study was to create a low-cost optical reader system that integrates with any smart device and accepts any type of rapid diagnostic test strip to provide fast and accurate data collection, sample analysis, and diagnostic result reporting. METHODS: An iterative design methodology was employed by a multidisciplinary research team to engineer three versions of a portable diagnostic testing device that were evaluated for usability and overall user receptivity. RESULTS: Repeated design critiques and usability studies identified a number of system requirements and considerations (eg, software compatibility, biomatter contamination, and physical footprint) that we worked to incrementally incorporate into successive system variants. Our final design phase culminated in the development of Tidbit, a reader that is compatible with any Wi-Fi-enabled device and test strip format. The Tidbit includes various features that support intuitive operation, including a straightforward test strip insertion point, external indicator lights, concealed electronic components, and an asymmetric shape, which inherently signals correct device orientation. Usability testing of the Tidbit indicates high usability for potential user communities. CONCLUSIONS: This study presents the design process, specification, and user reception of the Tidbit, an inexpensive, easy-to-use, portable optical reader for fast, accurate quantification of rapid diagnostic test results. Usability testing suggests that the reader is usable among and can benefit a wide group of potential users, including in POC contexts. Generally, the methodology of this study demonstrates the importance of testing these types of systems with potential users and exemplifies how iterative design processes can be employed by multidisciplinary research teams to produce compelling technological solutions.

NutriPhone: a mobile platform for low-cost point-of-care quantification of vitamin B12 concentrations.

Vitamin B12 is necessary for formation of red blood cells, DNA synthesis, neural myelination, brain development, and growth. Vitamin B12 deficiency is often asymptomatic early in its course; however, once it manifests, particularly with neurological symptoms, reversal by dietary changes or supplementation becomes less effective. Access to easy, low cost, and personalized nutritional diagnostics could enable individuals to better understand their own deficiencies as well as track the effects of dietary changes. In this work, we present the NutriPhone, a mobile platform for the analysis of blood vitamin B12 levels in 15 minutes. The NutriPhone technology comprises of a smartphone accessory, an app, and a competitive-type lateral flow test strip that quantifies vitamin B12 levels. To achieve the detection of sub-nmol/L physiological levels of vitamin B12, our assay incorporates an innovative "spacer pad" for increasing the duration of the key competitive binding reaction and uses silver amplification of the initial signal. We demonstrate the efficacy of our NutriPhone system by quantifying physiologically relevant levels of vitamin B12 and performing human trials where it was used to accurately evaluate blood vitamin B12 status of 12 participants from just a drop (~40 µl) of finger prick blood.