Empathy Detection Using Machine Learning on Text, Audiovisual, Audio or Physiological Signals

Empathy is a social skill that indicates an individual's ability to understand others. Over the past few years, empathy has drawn attention from various disciplines, including but not limited to Affective Computing, Cognitive Science and Psychology. Empathy is a context-dependent term; thus, detecting or recognising empathy has potential applications in society, healthcare and education. Despite being a broad and overlapping topic, the avenue of empathy detection studies leveraging Machine Learning remains underexplored from a holistic literature perspective. To this end, we systematically collect and screen 801 papers from 10 well-known databases and analyse the selected 54 papers. We group the papers based on input modalities of empathy detection systems, i.e., text, audiovisual, audio and physiological signals. We examine modality-specific pre-processing and network architecture design protocols, popular dataset descriptions and availability details, and evaluation protocols. We further discuss the potential applications, deployment challenges and research gaps in the Affective Computing-based empathy domain, which can facilitate new avenues of exploration. We believe that our work is a stepping stone to developing a privacy-preserving and unbiased empathic system inclusive of culture, diversity and multilingualism that can be deployed in practice to enhance the overall well-being of human life

Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond

In the past decade, the deployment of deep learning (Artificial Intelligence (AI)) methods has become pervasive across a spectrum of real-world applications, often in safety-critical contexts. This comprehensive research article rigorously investigates the ethical dimensions intricately linked to the rapid evolution of AI technologies, with a particular focus on the healthcare domain. Delving deeply, it explores a multitude of facets including transparency, adept data management, human oversight, educational imperatives, and international collaboration within the realm of AI advancement. Central to this article is the proposition of a conscientious AI framework, meticulously crafted to accentuate values of transparency, equity, answerability, and a human-centric orientation. The second contribution of the article is the in-depth and thorough discussion of the limitations inherent to AI systems. It astutely identifies potential biases and the intricate challenges of navigating multifaceted contexts. Lastly, the article unequivocally accentuates the pressing need for globally standardized AI ethics principles and frameworks. Simultaneously, it aptly illustrates the adaptability of the ethical framework proposed herein, positioned skillfully to surmount emergent challenges

Jefferson Alumni Bulletin – Volume 52, Number 4, September 2003

Jefferson Alumni Bulletin – Volume 52, Number 4, September 2003 The Dean\u27s Column by Thomas J. Nasca ’75, page 4 The Orbit of My Eye an essay by Risa Ravitz ’05, page 8 Students Teach Underserved Children to Read, in Addition to Providing Medical Care for the Parents, page 10 Combining Two Types of Radiation Therapy to Improve the Treatment of Brain Cancer, page 17 Landmark Study of Early Surgery for Epilepsy, page 17 Research on How to Improve Physician Empathy , page 18 Using Plants to Produce Antibodies Against Rabies, page 19 HIV\u27s Escape Route from Drugs and Vaccines Is Found, page 19 From Israel to Hong Kong: Jeffersonians Abroad, page 24 Carolyn Runowicz \u2777, Director of the University of Connecticut Cancer Center, page 34 Class Notes attached as supplemental file along with campus features article

Future care: rethinking technology enhanced aged care environments

© 2018, Emerald Publishing Limited. Purpose: Cutting-edge hospital and residential care architecture and interior design aim to address the emotional and practical needs of patients, staff and visitors. Yet, whilst improving on past practice, current approaches to design still rarely recognise or respond to individuals. The purpose of this paper is to provide a review of design-led research into digital technology across disciplines for the personalisation of healthcare environments and is informed by the authors’ ongoing hospital-based research. Design/methodology/approach: This review is based on a design anthropology framework providing insight into designing for changing the experience for older patients in current healthcare contexts and future focused strategies, integrating digital technologies and human-centred design across scale and disciplines. It is informed by ongoing hospital studies based on design-led research methodology, drawing on design anthropology and ethnographical methods. Findings: Technology enhanced, human-centred, assistive devices and environments implemented into healthcare across scale are developing but integration is needed for meaningful experiences. Research limitations/implications: This review is a positioning paper for design-led research into digital technology across scale and medium. Practical implications: This paper provides the basis for practical research including the ongoing hospital-based research of the authors. Social implications: This approach potentially enhances emotional experiences of connected healthcare. Originality/value: Future care scenarios are proposed, with technology and human experience as key drivers. Individualised and personalised solutions better cater for diversity. Within this context, it is strategic to question and test new ways of crafting the older persons care experience. This paper brings new direction to this discussion

Artificial Intelligence Accelerated Transformation in The Healthcare Industry

The healthcare industry was a pioneer in the deployment of artificial intelligence (AI) technology. Due to the nature of the services and the vulnerability of a sizable portion of end users, there has been a significant amount of research and discussion on the concept of artificial intelligence. A mixed-method approach has been used to pinpoint the components of moral AI in the healthcare sector and look into how it affects value creation and market performance. Since AI technology is still developing in India, analysis is conducted in an Indian context. The understanding of how various AI components supported healthcare organisations and deliver better patient-centered care and evidence-based medicine was aided by these in-depth studies and analyses of the patient perspective

Communicative Development and Diffusion of Humanoid AI Robots for the Post-Pandemic Health Care System

As humanoid robot technology, anthropomorphized by artificial intelligence (AI), has rapidly advanced to introduce more human-resembling automated robots that can communicate, interact, and work like humans, we have begun to expect active interactions with Humanoid AI Robots (HAIRs) in the near future. Coupled with the HAIR technology development, the COVID-19 pandemic triggered our interest in using health care robots with many substantial advantages that overcome critical human vulnerabilities against the strong infectious COVID-19 virus. Recognizing the tremendous potential for the active application of HAIRs, this article explores feasible ways to implement HAIRs in health care and patient services and suggests recommendations for strategically developing and diffusing autonomous HAIRs in health care facilities. While discussing the integration of HAIRs into health care, this article points out some important ethical concerns that should be addressed for implementing HAIRs for health care services

Understanding Advice Sharing among Physicians: Towards Trust-Based Clinical Alerts

Safe prescribing of medications relies on drug safety alerts, but up to 96% of such warnings are ignored by physicians. Prior research has proposed improvements to the design of alerts, but with limited increase in adherence. We propose a different perspective: before re-designing alerts, we focus on improving the trust between physicians and computerized advice by examining why physicians trust their medical colleagues. To understand trusted advice among physicians, we conducted three contextual inquiries in a hospital setting (22 participants), and corroborated our findings with a survey (37 participants). Drivers that guide physicians in trusting peer advice include: timeliness of the advice, collaborative language, empathy, level of specialization and medical hierarchy. Based on these findings, we introduce seven design directions for trust-based alerts: endorsement, transparency, team sensing, collaborative, empathic, conflict mitigating and agency laden. Our work contributes to novel alert design strategies to improve the effectiveness of drug safety advice