Questions and Answers About Blindness and Vision Impairments in the Workplace and the Americans with Disabilities Act

The Americans with Disabilities Act (ADA) is a federal law that prohibits discrimination on the basis of disability. Title I of the ADA makes it unlawful for any employer to discriminate against a qualified applicant or employee because of a disability in any aspect of employment. The ADA covers employers with 15 or more employees, including state and local governments. Section 501 of the Rehabilitation Act provides the same protections for federal government employees and applicants. In addition, most states have their own laws prohibiting employment discrimination on the basis of disability. Some of these state laws may apply to smaller employers and provide protections in addition to those available under the ADA

Human-AI Collaboration in Healthcare: A Review and Research Agenda

Advances in Artificial Intelligence (AI) have led to the rise of human-AI collaboration. In healthcare, such collaboration could mitigate the shortage of qualified healthcare workers, assist overworked medical professionals, and improve the quality of healthcare. However, many challenges remain, such as investigating biases in clinical decision-making, the lack of trust in AI and adoption issues. While there is a growing number of studies on the topic, they are in disparate fields, and we lack a summary understanding of this research. To address this issue, this study conducts a literature review to examine prior research, identify gaps, and propose future research directions. Our findings indicate that there are limited studies about the evolving and interactive collaboration process in healthcare, the complementarity of humans and AI, the adoption and perception of AI, and the long-term impact on individuals and healthcare organizations. Additionally, more theory-driven research is needed to inform the design, implementation, and use of collaborative AI for healthcare and to realize its benefits

Implementation and Application of Artificial Intelligence in Selected Public Services

Data-intensive technologies, such as artificial intelligence, imply huge opportunities for transforming the delivery of healthcare and social services, improving people’s quality of life and working in the health and welfare system. The aim of this paper is to present examples of the implementation of artificial intelligence techniques in healthcare and social services and to sketch the trends and challenges in the adoption of artificial intelligence techniques, with an emphasis on the public sector and selected public services. Analysis is based on a realistic assessment of current artificial intelligence technologies and their anticipated development. Besides the benefits and potential opportunities for healthcare and social services, there are also challenges for governments. Understanding the huge potential of artificial intelligence as well as its limitations will be a key step forward, but it is essential to avoid the trap of an overestimation of artificial intelligence potential

How a Diverse Research Ecosystem Has Generated New Rehabilitation Technologies: Review of NIDILRR’s Rehabilitation Engineering Research Centers

Over 50 million United States citizens (1 in 6 people in the US) have a developmental, acquired, or degenerative disability. The average US citizen can expect to live 20% of his or her life with a disability. Rehabilitation technologies play a major role in improving the quality of life for people with a disability, yet widespread and highly challenging needs remain. Within the US, a major effort aimed at the creation and evaluation of rehabilitation technology has been the Rehabilitation Engineering Research Centers (RERCs) sponsored by the National Institute on Disability, Independent Living, and Rehabilitation Research. As envisioned at their conception by a panel of the National Academy of Science in 1970, these centers were intended to take a “total approach to rehabilitation”, combining medicine, engineering, and related science, to improve the quality of life of individuals with a disability. Here, we review the scope, achievements, and ongoing projects of an unbiased sample of 19 currently active or recently terminated RERCs. Specifically, for each center, we briefly explain the needs it targets, summarize key historical advances, identify emerging innovations, and consider future directions. Our assessment from this review is that the RERC program indeed involves a multidisciplinary approach, with 36 professional fields involved, although 70% of research and development staff are in engineering fields, 23% in clinical fields, and only 7% in basic science fields; significantly, 11% of the professional staff have a disability related to their research. We observe that the RERC program has substantially diversified the scope of its work since the 1970’s, addressing more types of disabilities using more technologies, and, in particular, often now focusing on information technologies. RERC work also now often views users as integrated into an interdependent society through technologies that both people with and without disabilities co-use (such as the internet, wireless communication, and architecture). In addition, RERC research has evolved to view users as able at improving outcomes through learning, exercise, and plasticity (rather than being static), which can be optimally timed. We provide examples of rehabilitation technology innovation produced by the RERCs that illustrate this increasingly diversifying scope and evolving perspective. We conclude by discussing growth opportunities and possible future directions of the RERC program

One-Class Classification: Taxonomy of Study and Review of Techniques

One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure

Current Trends in Occupational Therapy Low Vision Rehabilitation

In the U.S. the number of adults age 40 and older who are blind or have low vision is 3.3 million (National Eye Institute, 2004) and it is expected to reach 5.5 million by the year 2020 (U.S. Department of Health and Human Services, 2011). The background and training occupational therapists receive on disability and aging puts them in a position to serve this population of individuals with low vision concerns. This study described the level of knowledge, confidence and practices of occupational therapists in the U.S. who work in physical disability settings with regard to the assessment and treatment of adult clients with low vision. A survey was mailed to 250 currently practicing occupational therapists with 58 of those returned meeting the inclusion criteria. Overall, respondents reported reasonable knowledge and confidence related to evaluating and treating adult clients with low vision. However, thirty-two respondents indicated that they felt additional education or training was needed to provide effective low vision treatment for their clients. This is despite the fact that most had entry-level education and continuing education that addressed low vision rehabilitation. It is essential that occupational therapists have the basic knowledge, tools and resources to effectively and confidently assess and treat the millions of Americans with low vision