Towards healthcare personal agents

Riccardi, Giuseppe

doi:10.1145/2666253.2666266

Cited by 26 publications

(21 citation statements)

References 12 publications

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“…Such cases rely upon a more sophisticated conversational purpose and knowledge base, and the level of AI complexity rises with a deeper understanding by the AI agent through data accumulation. It may be necessary to incorporate aspects of affective behaviour, using multimodal contextual awareness mechanisms to enable an authentic conversational dialogue 25 . For example, if issues arising from a patient’s past interactions or past medical history need to be considered in making conversational decisions, a personalised model of the individual’s context will be needed in addition to the context model for the current conversation.…”

Section: Telehealth Ai and New Models Of Carementioning

confidence: 99%

Role of Artificial Intelligence within the Telehealth Domain

et al. 2019

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Objectives: This paper provides a discussion about the potential scope of applicability of Artificial Intelligence methods within the telehealth domain. These methods are focussed on clinical needs and provide some insight to current directions, based on reports of recent advances. Methods: Examples of telehealth innovations involving Artificial Intelligence to support or supplement remote health care delivery were identified from recent literature by the authors, on the basis of expert knowledge. Observations from the examples were synthesized to yield an overview of contemporary directions for the perceived role of Artificial Intelligence in telehealth. Results: Two major focus areas for related contemporary directions were established. These were first, quality improvement for existing clinical practice and service delivery, and second, the development and support of new models of care. Case studies from each focus area have been chosen for illustration purposes. Conclusion: Examples of the role of Artificial Intelligence in delivery of health care remotely include use of tele-assessment, tele-diagnosis, tele-interactions, and tele-monitoring. Further developments of underlying algorithms and validation of methods will be required for wider adoption. Certain key social and ethical considerations also need consideration more generally in the health system, as Artificial-Intelligence-enabled-telehealth becomes more commonplace.

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Section: Telehealth Ai and New Models Of Carementioning

confidence: 99%

Role of Artificial Intelligence within the Telehealth Domain

et al. 2019

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“…A lot of progress have been made in the field of AI applications and advances in machine learning had made possible the design of systems that are able to learn from available information to develop different kinds of systems 8 . Studies have reported on several interventions in the field of surgery and assistive therapies especially in geriatrics and with disabled patients 9 .…”

Section: Artificial Intelligence and Roboticsmentioning

confidence: 99%

Robots to the Rescue: A Review of Studies on Differential Medical Diagnosis Employing Ontology-Based Chat Bot Technology

Edwards¹,

Muniru²,

Cheok³

2016

Preprint

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Access to medical care is a global issue. Technology-aided approaches have been applied in addressing this. Interventions have however not focused on medical diagnosis as a fully automated procedure and available applications employ mainly text-based inputs rather than conversation in natural language. We explored the utility of ontology-based chatbot technology for the design of intelligent agents for medical diagnosis through a systematic review of the most recent related literature. English articles published in 2011-2016 returned 233 hits which yielded 11 relevant articles after a 3-stage screening. Findings showed that the creation of expert systems had been the focus of many the studies which utilize the physician-system-patient framework with system training based mostly on expert knowledge for designing web-or mobile phone-based applications that serve assistive purposes. Findings further indicated gaps in the design and evaluation of more effective systems deployable as standalone applications, for example, on an embodied robotic system. The need for technology supporting the physical examination part of diagnosis, connection to data sources on patients' vitals and medical history are also indicated in addition to the need for more qualitative work on natural language-based interaction. The system should be one that is continuously learning. Future works should also be directed towards the building of more robust knowledge base as well as evaluation of theory-based diagnostic methodological options

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“…Virtual Personal Assistant (VPA) is a software agent that provides professional administrative, technical, or social assistance to a human user [25]. Apple's Siri (http://en.wikipedia.org/wiki/ Siri) started the trend for VPA by providing a speech interface for questions/answers in 2011.…”

Section: Introductionmentioning

confidence: 99%

A model to measure QoE for virtual personal assistant

Saad

Afzal

El-Issawi

et al. 2016

Multimed Tools Appl

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Until now the virtual assistants (like Siri, Google Now and Cortana) have primarily been confined to voice input and output only. Is there a justification for voice only confinement or can we enhance the user experience by adding a visual output? We hypothesized that providing a higher level of visual/auditory immersion would enhance the quality of user experience. In order to test this hypothesis, we first developed 4 variants of virtual assistant, each with a different audio/visual level of immersion. Developed virtual assistant systems were the following; audio only, audio and 2D visual display, audio and 3D visual display and audio and immersive 3D visual display. We developed a plan for usability testing of all 4 variants. The usability testing was conducted with 30 subjects against eight (8) dependent variables included presence, involvement, attention, reliability, dependency, easiness, satisfaction and expectations. Each subject rated these dependent variables based on a scale of 1-5, 5 being the highest value. The raw data collected from usability testing was then analyzed through several tools in order to determine the factors contributing towards the quality of experience for each of the 4 variants. The significant factors were then used develop a model that measures the quality of user experience. It was found that each variant had a different set of significant variables. Hence, in order to rate each system there is a need to develop a scale that is dependent upon the unique set of variables for the respective variant. Furthermore, it was found that variant 4 scored the highest rate for Quality of Experience (QoE). Lastly several other qualitative conclusions were also drawn from this research that will guide future work in the field of virtual assistants.

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Towards healthcare personal agents

Cited by 26 publications

References 12 publications

Role of Artificial Intelligence within the Telehealth Domain

Role of Artificial Intelligence within the Telehealth Domain

Robots to the Rescue: A Review of Studies on Differential Medical Diagnosis Employing Ontology-Based Chat Bot Technology

A model to measure QoE for virtual personal assistant

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