Smart-bot Technology: Conversational Agents Role in Maternal Healthcare Support

Mugoye, K.; Okoyo, Henry O.; McOyowo, Sylvester O.

doi:10.23919/istafrica.2019.8764817

Cited by 21 publications

(6 citation statements)

References 3 publications

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“…• Providing maternal, prenatal, antenatal or infant care (n = 10): [48], [148]- [156]. • Health monitoring (n = 5): [148], [151], [155], [157], [158], from which specifically home monitoring [151], [158]. • Evaluation of health programs [159] (exploratory protocol), reducing health care variability [160] (IDSS) and health resources allocation [158] (wearable).…”

Section: K Maternal and Fetus Well-beingmentioning

confidence: 99%

Artificial Intelligence in Pregnancy: A Scoping Review

et al. 2020

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Artificial Intelligence has been widely applied to a majority of research areas, including health and medicine. Certain complications or disorders that can appear during pregnancy can endanger the life of both mother and fetus. There is enough scientific literature to support the idea that emotional aspects can be a relevant risk factor in pregnancy (such as anxiety, stress or depression, for instance). This paper presents a scoping review of the scientific literature from the past 12 years (2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020) to identify which methodologies, techniques, algorithms and frameworks are used in Artificial Intelligence and Affective Computing for pregnancy health and well-being. The methodology proposed by Arksey and O'Malley, in conjunction with PRISMA-ScR framework has been used to create this review. Despite the relevance that emotional status can have as a risk factor during pregnancy, one of the main findings of this study is that there is still not a significant amount of literature on automatic analysis of emotion. Health enhancement and well-being for pregnant women can be achieved with artificial intelligence or affective computing based devices, hence future work on this topic is strongly suggested.

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Section: K Maternal and Fetus Well-beingmentioning

confidence: 99%

Artificial Intelligence in Pregnancy: A Scoping Review

et al. 2020

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“…The initial search identified 212 articles, with no duplicates. After screening, 200 articles were excluded, and 12 studies were included (see Multimedia Appendix 1 [11][12][13][14][15][16][17][18][19][20][21][22] for the full list).…”

Section: Overviewmentioning

confidence: 99%

Health Chatbots in Africa: Scoping Review

Phiri¹,

Chambwera²

2023

J Med Internet Res

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Background This scoping review explores and summarizes the existing literature on the use of chatbots to support and promote health in Africa. Objective The primary aim was to learn where, and under what circumstances, chatbots have been used effectively for health in Africa; how chatbots have been developed to the best effect; and how they have been evaluated by looking at literature published between 2017 and 2022. A secondary aim was to identify potential lessons and best practices for others chatbots. The review also aimed to highlight directions for future research on the use of chatbots for health in Africa. Methods Using the 2005 Arksey and O’Malley framework, we used a Boolean search to broadly search literature published between January 2017 and July 2022. Literature between June 2021 and July 2022 was identified using Google Scholar, EBSCO information services—which includes the African HealthLine, PubMed, MEDLINE, PsycInfo, Cochrane, Embase, Scopus, and Web of Science databases—and other internet sources (including gray literature). The inclusion criteria were literature about health chatbots in Africa published in journals, conference papers, opinion, or white papers. Results In all, 212 records were screened, and 12 articles met the inclusion criteria. Results were analyzed according to the themes they covered. The themes identified included the purpose of the chatbot as either providing an educational or information-sharing service or providing a counselling service. Accessibility as a result of either technical restrictions or language restrictions was also noted. Other themes that were identified included the need for the consideration of trust, privacy and ethics, and evaluation. Conclusions The findings demonstrate that current data are insufficient to show whether chatbots are effectively supporting health in the region. However, the review does reveal insights into popular chatbots and the need to make them accessible through language considerations, platform choice, and user trust, as well as the importance of robust evaluation frameworks to assess their impact. The review also provides recommendations on the direction of future research.

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“…Ireland et al, proposed 'Harlie', which converses with the user on a variety of topics and helps in the neurological conditions of Parkinson's patients [36]. Similarly, a virtual nurse was proposed in [37] to support maternal healthcare and provide guidance to expectant mothers during pregnancy. A few smartphone applications are also available that provide medical information after the analysis of symptoms such as MedWhat [20], Your.MD [21], and Sensely [22].…”

Section: Frame-based Assistantsmentioning

confidence: 99%

Medical Instructed Real-Time Assistant for Patient with Glaucoma and Diabetic Conditions

et al. 2020

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Virtual assistants are involved in the daily activities of humans such as managing calendars, making appointments, and providing wake-up calls. They provide a conversational service to customers around-the-clock and make their daily life manageable. With this emerging trend, many well-known companies launched their own virtual assistants that manage the daily routine activities of customers. In the healthcare sector, virtual medical assistants also provide a list of relevant diseases linked to a specific symptom. Due to low accuracy and uncertainty, these generated recommendations are untrusted and may lead to hypochondriasis. In this study, we proposed a Medical Instructed Real-time Assistant (MIRA) that listens to the user’s chief complaint and predicts a specific disease. Instead of informing about the medical condition, the user is referred to a nearby appropriate medical specialist. We designed an architecture for MIRA that considers the limitations of existing virtual medical assistants such as weak authentication, lack of understanding multiple intent statements about a specific medical condition, and uncertain diagnosis recommendations. To implement the designed architecture, we collected the chief complaints along with the dialogue corpora of real patients. Then, we manually validated these data under the supervision of medical specialists. We then used these data for natural language understanding, disease identification, and appropriate response generation. For the prototype version of MIRA, we considered the cases of glaucoma (eye disease) and diabetes (an autoimmune disease) only. The performance measure of MIRA was evaluated in terms of accuracy (89%), precision (90%), sensitivity (89.8%), specificity (94.9%), and F-measure (89.8%). The task completion was calculated using Cohen’s Kappa ( k = 0.848 ) that categorizes MIRA as ‘Almost Perfect’. Furthermore, the voice-based authentication identifies the user effectively and prevent against masquerading attack. Simultaneously, the user experience shows relatively good results in all aspects based on the User Experience Questionnaire (UEQ) benchmark data. The experimental results show that MIRA efficiently predicts a disease based on chief complaints and supports the user in decision making.

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Smart-bot Technology: Conversational Agents Role in Maternal Healthcare Support

Cited by 21 publications

References 3 publications

Artificial Intelligence in Pregnancy: A Scoping Review

Artificial Intelligence in Pregnancy: A Scoping Review

Health Chatbots in Africa: Scoping Review

Medical Instructed Real-Time Assistant for Patient with Glaucoma and Diabetic Conditions

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