Scalable diagnostic screening of mild cognitive impairment using AI dialogue agent

Tang, Fengyi; Ikechukwu, Uchendu,; Wang, Fei; Dodge, Hiroko H.; Zhou, Jiayu

doi:10.1038/s41598-020-61994-0

Cited by 17 publications

(23 citation statements)

References 37 publications

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“…Recently, a clinical trial involving extensive interviews between patients and trained medical staff using linguistic markers as screening tools for mild cognitive impairment (MCI) detection has shown promise 62 , 63 . Tang et al 64 built a conversational agent based on transcripts from these clinical trials using reinforcement learning techniques 65 . This agent was trained to maximize the diagnosis accuracy of MCI with a minimum number of conversational events, and the agent performed significantly better than supervised learning models.…”

Section: Ai For Common Biomedical Data Typesmentioning

confidence: 99%

AI in Health: State of the Art, Challenges, and Future Directions

2019

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Introduction: Artificial intelligence (AI) technologies continue to attract interest from a broad range of disciplines in recent years, including health. The increase in computer hardware and software applications in medicine, as well as digitization of health-related data together fuel progress in the development and use of AI in medicine. This progress provides new opportunities and challenges, as well as directions for the future of AI in health. Objective: The goals of this survey are to review the current state of AI in health, along with opportunities, challenges, and practical implications. This review highlights recent developments over the past five years and directions for the future. Methods: Publications over the past five years reporting the use of AI in health in clinical and biomedical informatics journals, as well as computer science conferences, were selected according to Google Scholar citations. Publications were then categorized into five different classes, according to the type of data analyzed. Results: The major data types identified were multi-omics, clinical, behavioral, environmental and pharmaceutical research and development (R&D) data. The current state of AI related to each data type is described, followed by associated challenges and practical implications that have emerged over the last several years. Opportunities and future directions based on these advances are discussed. Conclusion: Technologies have enabled the development of AI-assisted approaches to healthcare. However, there remain challenges. Work is currently underway to address multi-modal data integration, balancing quantitative algorithm performance and qualitative model interpretability, protection of model security, federated learning, and model bias.

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Section: Ai For Common Biomedical Data Typesmentioning

confidence: 99%

AI in Health: State of the Art, Challenges, and Future Directions

2019

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“…The target of these interfaces is to screen for additional markers to the linguistic ones, such as motor skills and auditory skills. Systems like those described by Tang et al [4] and Mirheidari et al [15] utilise VUI through several modalities, such as intelligent virtual agents (IVAs), robots, virtual assistants (VAs), and regular voice recorders. Naturally, this decision affects the nature of the screening test that accompanies these interfaces because its content should only support voice input and its results should be based on linguistic markers.…”

Section: User Interfacementioning

confidence: 99%

“…The literature review resulted in five modalities that are currently in speech-based systems: IVAs, VAs, socially assistive robots (SAR), audio recorders and telephone audio recorders. IVAs can 'learn' and be trained, thus enabling valuable real-time conversations between the system and the user [4] while VAs and SARs can provide screening tasks, recognise voice commands and facilitate multimodal interaction, as required from structured cognitive screening tests [14,19,23]. Voice recorders and telephones are less technologically advanced devices, which, however, can facilitate a less automated type of interaction that usually requires the assistance from a human screener to administer the cognitive test [12,18].…”

Section: Modalitiesmentioning

confidence: 99%

“…Research has shown that MCI individuals with impairments in multiple domains, including language, are more likely to develop dementia compared to those with pure memory impairment [2]. Simple linguistic markers, such as word choice, verbal fluency, phrasing (i.e., 'utterance') and short speech patterns possess predictive power in assessing early cognitive decline status [1,4]. Language, speech and conversational behaviours reflect cognitive changes that may precede physiological changes and offer a much more cost-effective option for detecting preclinical cognitive decline [4,5].…”

Section: Introductionmentioning

confidence: 99%

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A Review of Automated Speech-Based Interaction for Cognitive Screening

Boletsis

2020

MTI

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Language, speech and conversational behaviours reflect cognitive changes that may precede physiological changes and offer a much more cost-effective option for detecting preclinical cognitive decline. Artificial intelligence and machine learning have been established as a means to facilitate automated speech-based cognitive screening through automated recording and analysis of linguistic, speech and conversational behaviours. In this work, a scoping literature review was performed to document and analyse current automated speech-based implementations for cognitive screening from the perspective of human–computer interaction. At this stage, the goal was to identify and analyse the characteristics that define the interaction between the automated speech-based screening systems and the users, potentially revealing interaction-related patterns and gaps. In total, 65 articles were identified as appropriate for inclusion, from which 15 articles satisfied the inclusion criteria. The literature review led to the documentation and further analysis of five interaction-related themes: (i) user interface, (ii) modalities, (iii) speech-based communication, (iv) screening content and (v) screener. Cognitive screening through speech-based interaction might benefit from two practices: (1) implementing more multimodal user interfaces that facilitate—amongst others—speech-based screening and (2) introducing the element of motivation in the speech-based screening process.

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“…At the same time, in the past decade, various voice-interactionbased devices and softwares (Dialogue Agents) such as smart speakers (e.g., Google Home and Amazon Echo) and voice assistants (e.g., Siri and Cortana) [13] have been developed and employed to provide useful information and a variety of services in response to users' prompts. In addition, there has been much research into the use of Dialogue Agents for older adults with dementia and mild cognitive impairment [14]- [17]. However, it should be noted that these devices and the Dialogue Agents generally do not talk to users without a specific request or prompt.…”

Section: Introductionmentioning

confidence: 99%