Safety First: Conversational Agents for Health Care

Bickmore, Timothy; Trinh, Ha; Asadi, Reza; Ólafsson, Stefán

doi:10.1007/978-3-319-95579-7_3

Cited by 33 publications

(33 citation statements)

References 63 publications

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“…Because conversational AI may augment the work of psychotherapy, we seek to encourage product designers, clinicians, and researchers to assess the impact of new practices on both patients and clinicians. Other areas of medicine have seen success with AI, such as lung cancer imaging and building diagnostic or prognostic models (69–73), and conversational AI for health is an emerging field with limited research on efficacy and safety (40, 63, 74).…”

Section: Discussionmentioning

confidence: 99%

“…Conversely, users were more reluctant to disclose sensitive information such as binge drinking behavior to a conversational AI compared to a non-responsive questionnaire (62). Because personal disclosures are central to diagnosis and treatment in psychotherapy, users’ expectations and behavior towards technology-mediated conversations merit further assessment (63, 64, 65).…”

Section: Dimensions Of Impactmentioning

confidence: 99%

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Key Considerations for Incorporating Conversational AI in Psychotherapy

et al. 2019

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Conversational artificial intelligence (AI) is changing the way mental health care is delivered. By gathering diagnostic information, facilitating treatment, and reviewing clinician behavior, conversational AI is poised to impact traditional approaches to delivering psychotherapy. While this transition is not disconnected from existing professional services, specific formulations of clinician-AI collaboration and migration paths between forms remain vague. In this viewpoint, we introduce four approaches to AI-human integration in mental health service delivery. To inform future research and policy, these four approaches are addressed through four dimensions of impact: access to care, quality, clinician-patient relationship, and patient self-disclosure and sharing. Although many research questions are yet to be investigated, we view safety, trust, and oversight as crucial first steps. If conversational AI isn’t safe it should not be used, and if it isn’t trusted, it won’t be. In order to assess safety, trust, interfaces, procedures, and system level workflows, oversight and collaboration is needed between AI systems, patients, clinicians, and administrators.

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Section: Discussionmentioning

confidence: 99%

Section: Dimensions Of Impactmentioning

confidence: 99%

Key Considerations for Incorporating Conversational AI in Psychotherapy

et al. 2019

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“…Several hospitals and medical centers engaged in interorganizational collaborations, but the number of such collaborations is still small. This may explain the existing challenge of insufficient clinical evaluation of CIs, inconsistent results, and high concern for patient safety, which were disclosed by previous studies [2,70,71]. In addition, the function of CIs in patient care requires substantial domain knowledge and an adequate understanding of human emotion [72].…”

Section: Discussionmentioning

confidence: 99%

Conversational Interfaces for Health: Bibliometric Analysis of Grants, Publications, and Patents

Xing¹,

Yu²,

Du³

et al. 2019

J Med Internet Res

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BackgroundConversational interfaces (CIs) in different modalities have been developed for health purposes, such as health behavioral intervention, patient self-management, and clinical decision support. Despite growing research evidence supporting CIs’ potential, CI-related research is still in its infancy. There is a lack of systematic investigation that goes beyond publication review and presents the state of the art from perspectives of funding agencies, academia, and industry by incorporating CI-related public funding and patent activities.ObjectiveThis study aimed to use data systematically extracted from multiple sources (ie, grant, publication, and patent databases) to investigate the development, research, and fund application of health-related CIs and associated stakeholders (ie, countries, organizations, and collaborators).MethodsA multifaceted search query was executed to retrieve records from 9 databases. Bibliometric analysis, social network analysis, and term co-occurrence analysis were conducted on the screened records.ResultsThis review included 42 funded projects, 428 research publications, and 162 patents. The total dollar amount of grants awarded was US $30,297,932, of which US $13,513,473 was awarded by US funding agencies and US $16,784,459 was funded by the Europe Commission. The top 3 funding agencies in the United States were the National Science Foundation, National Institutes of Health, and Agency for Healthcare Research and Quality. Boston Medical Center was awarded the largest combined grant size (US $2,246,437) for 4 projects. The authors of the publications were from 58 countries and 566 organizations; the top 3 most productive organizations were Northeastern University (United States), Universiti Teknologi MARA (Malaysia), and the French National Center for Scientific Research (CNRS; France). US researchers produced 114 publications. Although 82.0% (464/566) of the organizations engaged in interorganizational collaboration, 2 organizational research-collaboration clusters were observed with Northeastern University and CNRS as the central nodes. About 112 organizations from the United States and China filed 87.7% patents. IBM filed most patents (N=17). Only 5 patents were co-owned by different organizations, and there was no across-country collaboration on patenting activity. The terms patient, child, elderly, and robot were frequently discussed in the 3 record types. The terms related to mental and chronic issues were discussed mainly in grants and publications. The terms regarding multimodal interactions were widely mentioned as users’ communication modes with CIs in the identified records.ConclusionsOur findings provided an overview of the countries, organizations, and topic terms in funded projects, as well as the authorship, collaboration, content, and related information of research publications and patents. There is a lack of broad cross-sector partnerships among grant agencies, academia, and industry, particularly in the United States. Our results suggest a need to improve col...

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“…We utilized the Trindi Tick list to ascertain the comprehensive functionality of the dialogue engine's ability (Bickmore et al, 2018;Bos et al, 1999).…”

Section: Nlp-based Slave Functionsmentioning

confidence: 99%

Towards an Ontology-based Medication Conversational Agent for PrEP and PEP

Amith

Cui

Roberts

et al. 2020

Proceedings of the First Workshop on Natural Language Processing for Medical Conversations

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HIV (human immunodeficiency virus) can damage a human's immune system and cause Acquired Immunodeficiency Syndrome (AIDS) which could lead to severe outcomes, including death. While HIV infections have decreased over the last decade, there is still a significant population where the infection permeates. PrEP and PEP are two proven preventive measures introduced that involve periodic dosage to stop the onset of HIV infection. However, the adherence rates for this medication is low in part due to the lack of information about the medication. There exist several communication barriers that prevent patient-provider communication from happening. In this work, we present our ontologybased method for automating the communication of this medication that can be deployed for live conversational agents for PrEP and PEP. This method facilitates a model of automated conversation between the machine and user can also answer relevant questions.

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Safety First: Conversational Agents for Health Care

Cited by 33 publications

References 63 publications

Key Considerations for Incorporating Conversational AI in Psychotherapy

Key Considerations for Incorporating Conversational AI in Psychotherapy

Conversational Interfaces for Health: Bibliometric Analysis of Grants, Publications, and Patents

Towards an Ontology-based Medication Conversational Agent for PrEP and PEP

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