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TitleMedical Specialty Recommendations by an Artificial Intelligence Chatbot on a Smartphone: Development and Deployment.
AuthorsLee, H; Kang, J; Yeo, J
JournalJournal of medical Internet research
Publication Date6 May 2021
Date Added to PubMed22 Apr 2021
AbstractThe COVID-19 pandemic has limited daily activities and even contact between patients and primary care providers. This makes it more difficult to provide adequate primary care services, which include connecting patients to an appropriate medical specialist. A smartphone-compatible artificial intelligence (AI) chatbot that classifies patients' symptoms and recommends the appropriate medical specialty could provide a valuable solution. In order to establish a contactless method of recommending the appropriate medical specialty, this study aimed to construct a deep learning-based natural language processing (NLP) pipeline and to develop an AI chatbot that can be used on a smartphone. We collected 118,008 sentences containing information on symptoms with labels (medical specialty), conducted data cleansing, and finally constructed a pipeline of 51,134 sentences for this study. Several deep learning models, including 4 different long short-term memory (LSTM) models with or without attention and with or without a pretrained FastText embedding layer, as well as bidirectional encoder representations from transformers for NLP, were trained and validated using a randomly selected test data set. The performance of the models was evaluated on the basis of the precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). An AI chatbot was also designed to make it easy for patients to use this specialty recommendation system. We used an open-source framework called "Alpha" to develop our AI chatbot. This takes the form of a web-based app with a frontend chat interface capable of conversing in text and a backend cloud-based server application to handle data collection, process the data with a deep learning model, and offer the medical specialty recommendation in a responsive web that is compatible with both desktops and smartphones. The bidirectional encoder representations from transformers model yielded the best performance, with an AUC of 0.964 and F1-score of 0.768, followed by LSTM model with embedding vectors, with an AUC of 0.965 and F1-score of 0.739. Considering the limitations of computing resources and the wide availability of smartphones, the LSTM model with embedding vectors trained on our data set was adopted for our AI chatbot service. We also deployed an Alpha version of the AI chatbot to be executed on both desktops and smartphones. With the increasing need for telemedicine during the current COVID-19 pandemic, an AI chatbot with a deep learning-based NLP model that can recommend a medical specialty to patients through their smartphones would be exceedingly useful. This chatbot allows patients to identify the proper medical specialist in a rapid and contactless manner, based on their symptoms, thus potentially supporting both patients and primary care providers.
TitleArtificial Intelligence Chatbot Behavior Change Model for Designing Artificial Intelligence Chatbots to Promote Physical Activity and a Healthy Diet: Viewpoint.
AuthorsZhang, J; Oh, YJ; Lange, P; Yu, Z; Fukuoka, Y
JournalJournal of medical Internet research
Publication Date30 Sep 2020
Date Added to PubMed1 Oct 2020
AbstractChatbots empowered by artificial intelligence (AI) can increasingly engage in natural conversations and build relationships with users. Applying AI chatbots to lifestyle modification programs is one of the promising areas to develop cost-effective and feasible behavior interventions to promote physical activity and a healthy diet. The purposes of this perspective paper are to present a brief literature review of chatbot use in promoting physical activity and a healthy diet, describe the AI chatbot behavior change model our research team developed based on extensive interdisciplinary research, and discuss ethical principles and considerations. We conducted a preliminary search of studies reporting chatbots for improving physical activity and/or diet in four databases in July 2020. We summarized the characteristics of the chatbot studies and reviewed recent developments in human-AI communication research and innovations in natural language processing. Based on the identified gaps and opportunities, as well as our own clinical and research experience and findings, we propose an AI chatbot behavior change model. Our review found a lack of understanding around theoretical guidance and practical recommendations on designing AI chatbots for lifestyle modification programs. The proposed AI chatbot behavior change model consists of the following four components to provide such guidance: (1) designing chatbot characteristics and understanding user background; (2) building relational capacity; (3) building persuasive conversational capacity; and (4) evaluating mechanisms and outcomes. The rationale and evidence supporting the design and evaluation choices for this model are presented in this paper. As AI chatbots become increasingly integrated into various digital communications, our proposed theoretical framework is the first step to conceptualize the scope of utilization in health behavior change domains and to synthesize all possible dimensions of chatbot features to inform intervention design and evaluation. There is a need for more interdisciplinary work to continue developing AI techniques to improve a chatbot's relational and persuasive capacities to change physical activity and diet behaviors with strong ethical principles.
TitleDesign, Development, and Evaluation of a Telemedicine Platform for Patients With Sleep Apnea (Ognomy): Design Science Research Approach.
AuthorsMulgund, P; Sharman, R; Rifkin, D; Marrazzo, S
JournalJMIR formative research
Publication Date19 Jul 2021
Date Added to PubMed20 Jul 2021
AbstractWith an aging population and the escalating cost of care, telemedicine has become a societal imperative. Telemedicine alternatives are especially relevant to patients seeking care for sleep apnea, with its prevalence approaching one billion cases worldwide. Increasing awareness has led to a surge in demand for sleep apnea care; however, there is a shortage of the resources and expertise necessary to cater to the rising demand. The aim of this study is to design, develop, and evaluate a telemedicine platform, called Ognomy, for the consultation, diagnosis, and treatment of patients with sleep apnea. Using the design science research methodology, we developed a telemedicine platform for patients with sleep apnea. To explore the problem, in the analysis phase, we conducted two brainstorming workshops and structured interviews with 6 subject matter experts to gather requirements. Following that, we conducted three design and architectural review sessions to define and evaluate the system architecture. Subsequently, we conducted 14 formative usability assessments to improve the user interface of the system. In addition, 3 trained test engineers performed end-to-end system testing to comprehensively evaluate the platform. Patient registration and data collection, physician appointments, video consultation, and patient progress tracking have emerged as critical functional requirements. A telemedicine platform comprising four artifacts-a mobile app for patients, a web app for providers, a dashboard for reporting, and an artificial intelligence-based chatbot for customer onboarding and support-was developed to meet these requirements. Design reviews emphasized the need for a highly cohesive but loosely coupled interaction among the platform's components, which was achieved through a layered modular architecture using third-party application programming interfaces. In contrast, critical findings from formative usability assessments focused on the need for a more straightforward onboarding process for patients, better status indicators during patient registration, and reorganization of the appointment calendar. Feedback from the design reviews and usability assessments was translated into technical improvements and design enhancements that were implemented in subsequent iterations. Sleep apnea is an underdiagnosed and undertreated condition. However, with increasing awareness, the demand for quality sleep apnea care is likely to surge, and creative alternatives are needed. The results of this study demonstrate the successful application of a framework using a design science research paradigm to design, develop, and evaluate a telemedicine platform for patients with sleep apnea and their providers.
TitleCrowdsourcing for Creating a Dataset for Training a Medication Chatbot.
AuthorsZgraggen, CR; Kunz, SB; Denecke, K
JournalStudies in health technology and informatics
Publication Date27 May 2021
Date Added to PubMed28 May 2021
AbstractTo facilitate interaction with mobile health applications, chatbots are increasingly used. They realize the interaction as a dialog where users can ask questions and get answers from the chatbot. A big challenge is to create a comprehensive knowledge base comprising patterns and rules for representing possible user queries the chatbot has to understand and interpret. In this work, we assess how crowdsourcing can be used for generating examples of possible user queries for a medication chatbot. Within one week, the crowdworker generated 2'738 user questions. The examples provide a large variety of possible formulations and information needs. As a next step, these examples for user queries will be used to train our medication chatbot.
TitleA Smartphone-Based Health Care Chatbot to Promote Self-Management of Chronic Pain (SELMA): Pilot Randomized Controlled Trial.
AuthorsHauser-Ulrich, S; Künzli, H; Meier-Peterhans, D; Kowatsch, T
JournalJMIR mHealth and uHealth
Publication Date3 Apr 2020
Date Added to PubMed4 Apr 2020
AbstractOngoing pain is one of the most common diseases and has major physical, psychological, social, and economic impacts. A mobile health intervention utilizing a fully automated text-based health care chatbot (TBHC) may offer an innovative way not only to deliver coping strategies and psychoeducation for pain management but also to build a working alliance between a participant and the TBHC. The objectives of this study are twofold: (1) to describe the design and implementation to promote the chatbot painSELfMAnagement (SELMA), a 2-month smartphone-based cognitive behavior therapy (CBT) TBHC intervention for pain self-management in patients with ongoing or cyclic pain, and (2) to present findings from a pilot randomized controlled trial, in which effectiveness, influence of intention to change behavior, pain duration, working alliance, acceptance, and adherence were evaluated. Participants were recruited online and in collaboration with pain experts, and were randomized to interact with SELMA for 8 weeks either every day or every other day concerning CBT-based pain management (n=59), or weekly concerning content not related to pain management (n=43). Pain-related impairment (primary outcome), general well-being, pain intensity, and the bond scale of working alliance were measured at baseline and postintervention. Intention to change behavior and pain duration were measured at baseline only, and acceptance postintervention was assessed via self-reporting instruments. Adherence was assessed via usage data. From May 2018 to August 2018, 311 adults downloaded the SELMA app, 102 of whom consented to participate and met the inclusion criteria. The average age of the women (88/102, 86.4%) and men (14/102, 13.6%) participating was 43.7 (SD 12.7) years. Baseline group comparison did not differ with respect to any demographic or clinical variable. The intervention group reported no significant change in pain-related impairment (P=.68) compared to the control group postintervention. The intention to change behavior was positively related to pain-related impairment (P=.01) and pain intensity (P=.01). Working alliance with the TBHC SELMA was comparable to that obtained in guided internet therapies with human coaches. Participants enjoyed using the app, perceiving it as useful and easy to use. Participants of the intervention group replied with an average answer ratio of 0.71 (SD 0.20) to 200 (SD 58.45) conversations initiated by SELMA. Participants' comments revealed an appreciation of the empathic and responsible interaction with the TBHC SELMA. A main criticism was that there was no option to enter free text for the patients' own comments. SELMA is feasible, as revealed mainly by positive feedback and valuable suggestions for future revisions. For example, the participants' intention to change behavior or a more homogenous sample (eg, with a specific type of chronic pain) should be considered in further tailoring of SELMA. German Clinical Trials Register DRKS00017147;, Swiss National Clinical Trial Portal: SNCTP000002712;
TitleTrust Me, I'm a Chatbot: How Artificial Intelligence in Health Care Fails the Turing Test.
AuthorsPowell, J
JournalJournal of medical Internet research
Publication Date28 Oct 2019
Date Added to PubMed30 Oct 2019
AbstractOver the next decade, one issue which will dominate sociotechnical studies in health informatics is the extent to which the promise of artificial intelligence in health care will be realized, along with the social and ethical issues which accompany it. A useful thought experiment is the application of the Turing test to user-facing artificial intelligence systems in health care (such as chatbots or conversational agents). In this paper I argue that many medical decisions require value judgements and the doctor-patient relationship requires empathy and understanding to arrive at a shared decision, often handling large areas of uncertainty and balancing competing risks. Arguably, medicine requires wisdom more than intelligence, artificial or otherwise. Artificial intelligence therefore needs to supplement rather than replace medical professionals, and identifying the complementary positioning of artificial intelligence in medical consultation is a key challenge for the future. In health care, artificial intelligence needs to pass the implementation game, not the imitation game.
TitleMicroservice chatbot architecture for chronic patient support.
AuthorsRoca, S; Sancho, J; García, J; Alesanco, Á
JournalJournal of biomedical informatics
Publication Date1 Feb 2020
Date Added to PubMed18 Oct 2019
AbstractChatbots are able to provide support to patients suffering from very different conditions. Patients with chronic diseases or comorbidities could benefit the most from chatbots which can keep track of their condition, provide specific information, encourage adherence to medication, etc. To perform these functions, chatbots need a suitable underlying software architecture. In this paper, we introduce a chatbot architecture for chronic patient support grounded on three pillars: scalability by means of microservices, standard data sharing models through HL7 FHIR and standard conversation modeling using AIML. We also propose an innovative automation mechanism to convert FHIR resources into AIML files, thus facilitating the interaction and data gathering of medical and personal information that ends up in patient health records. To align the way people interact with each other using messaging platforms with the chatbot architecture, we propose these very same channels for the chatbot-patient interaction, paying special attention to security and privacy issues. Finally, we present a monitored-data study performed in different chronic diseases, and we present a prototype implementation tailored for one specific chronic disease, psoriasis, showing how this new architecture allows the change, the addition or the improvement of different parts of the chatbot in a dynamic and flexible way, providing a substantial improvement in the development of chatbots used as virtual assistants for chronic patients.
TitleAn artificial intelligence-based first-line defence against COVID-19: digitally screening citizens for risks via a chatbot.
AuthorsMartin, A; Nateqi, J; Gruarin, S; Munsch, N; Abdarahmane, I; Zobel, M; Knapp, B
JournalScientific reports
Publication Date4 Nov 2020
Date Added to PubMed6 Nov 2020
AbstractTo combat the pandemic of the coronavirus disease 2019 (COVID-19), numerous governments have established phone hotlines to prescreen potential cases. These hotlines have struggled with the volume of callers, leading to wait times of hours or, even, an inability to contact health authorities. Symptoma is a symptom-to-disease digital health assistant that can differentiate more than 20,000 diseases with an accuracy of more than 90%. We tested the accuracy of Symptoma to identify COVID-19 using a set of diverse clinical cases combined with case reports of COVID-19. We showed that Symptoma can accurately distinguish COVID-19 in 96.32% of clinical cases. When considering only COVID-19 symptoms and risk factors, Symptoma identified 100% of those infected when presented with only three signs. Lastly, we showed that Symptoma's accuracy far exceeds that of simple "yes-no" questionnaires widely available online. In summary, Symptoma provides unparalleled accuracy in systematically identifying cases of COVID-19 while also considering over 20,000 other diseases. Furthermore, Symptoma allows free text input, furthered with disease-specific follow up questions, in 36 languages. Combined, these results and accessibility give Symptoma the potential to be a key tool in the global fight against COVID-19. The Symptoma predictor is freely available online at .
TitlePreliminary Evaluation of a mHealth Coaching Conversational Artificial Intelligence for the Self-Care Management of People with Sickle-Cell Disease.
AuthorsIssom, DZ; Rochat, J; Hartvigsen, G; Lovis, C
JournalStudies in health technology and informatics
Publication Date16 Jun 2020
Date Added to PubMed24 Jun 2020
AbstractAdherence to the complex set of recommended self-care practices among people with Sickle-Cell Disease (SCD) positively impacts health outcomes. However, few patients possess the required skills (i.e. disease-specific knowledge, adequate levels of self-efficacy). Consequently, adherence rates remain low and only 1% of patients are empowered enough to master the self-care practices. Health coaching and therapeutic patient education have emerged as new approaches to enhance patients' self-management and support health behavior changes. This preliminary feasibility study examined patients' perceived usefulness of the information provided by a chatbot we developed following patient-important requirements collected during our preliminary studies. Participants tested the chatbot and completed a post-test survey. A total of 19 patients were enrolled and 2 withdrew. 15 respondents (15/17, 88%) gave a score of at least 3/4 to the question "The chatbot contains all the information I need". Results suggest that mHealth coaching apps could be used to promote the knowledge acquisition of recommended health behaviors related to the prevention of SCD main symptoms.
TitleUnguided Chatbot-Delivered Cognitive Behavioural Intervention for Problem Gamblers Through Messaging App: A Randomised Controlled Trial.
AuthorsSo, R; Furukawa, TA; Matsushita, S; Baba, T; Matsuzaki, T; Furuno, S; Okada, H; Higuchi, S
JournalJournal of gambling studies
Publication Date1 Dec 2020
Date Added to PubMed13 Mar 2020
AbstractInternet-delivered intervention may be an acceptable alternative for the more than 90% of problem gamblers who are reluctant to seek face-to-face support. Thus, we aimed to (1) develop a low-dropout unguided intervention named GAMBOT integrated with a messaging app; and (2) investigate its effect. The present study was a randomised, quadruple-blind, controlled trial. We set pre-to-post change in the Problem Gambling Severity Index (PGSI) as the primary outcome and pre-to-post change in the Gambling Symptom Assessment Scale (G-SAS) as a secondary outcome. Daily monitoring, personalised feedback, and private messages based on cognitive behavioural theory were offered to participants in the intervention group through a messaging app for 28 days (GAMBOT). Participants in the control group received biweekly messages only for assessments for 28 days (assessments only). A total of 197 problem gamblers were included in the primary analysis. We failed to demonstrate a significant between-group difference in the primary outcome (PGSI - 1.14, 95% CI - 2.75 to 0.47, p = 0.162) but in the secondary outcome (G-SAS - 3.14, 95% CI - 0.24 to - 6.04, p = 0.03). Only 6.7% of the participants dropped out during follow-up and 77% of the GAMBOT group participants (74/96) continued to participate in the intervention throughout the 28-day period. Integrating intervention into a chatbot feature on a frequently used messaging app shows promise in helping to overcome the high dropout rate of unguided internet-delivered interventions. More effective and sophisticated contents delivered by a chatbot should be sought to engage over 90% of problem gamblers who are reluctant to seek face-to-face support.
MNCHFPRHHIV/AIDSMalariaNoncommunicable diseaseCOVID-19Decision-makingEducation & trainingBehavior changeGovernancePrivacy & securityEquityCHWsYouth & adolescentsSystematic reviewsProtocols & research designMedical RecordsLaboratoryPharmacyHuman ResourcesmHealthSMSChatbotsAI