Digital Medical Interview Assistant AnCha for Obtaining the Medical History in General Medicine: Case study (Preprint)

BACKGROUND Collecting information on the medical history of a patient is an important step during the diagnosing process. Besides the interrogation by the physician, computerized questionnaires are used to collect the data. To facilitate interaction, implementation of digital medical interview assistants (DMIA) using conversational user interfaces (CUI) gain in interest. OBJECTIVE The aim of this research is to assess patient’s and physician’s perceptions towards a DMIA with CUI. Beyond, we want to understand how such DMIA can be used in real-world context, what issues and barriers exist in their usage. METHODS We developed a web-based DMIA with CUI (referred to as AnCha for anamnesis chatbot) as a research prototype in a participative and iterative development process. We conducted a pilot trial in a practice for general medicine. Patient perceptions were collected and physicians were interrogated regarding usefulness of collected information. RESULTS 31 patients were approached, and 9 participants were included in the pilot trial; 3 conversation protocols were used by the physicians to prepare for the encounter. Participants spanned all age groups from digital natives (n=5), and digital workers (n=3) to digital seniors (n=1). Patients can easily interact with AnCha and are willing to provide information to the digital tool. They recognize benefits while using the dialog system compared to the existing process. Important insights into practical implementation and integration into practice workflows could be gained. CONCLUSIONS Providing information on complaints and medical history before the actual encounter is considered useful. In order to be supportive for physicians, information has to be made available in a sufficient time frame before the encounter. Future work has to assess in particular whether AnCha is also well accessible for digital seniors.

Diabetes and conversational agents: the AIDA project case study

One of the key aspects in the process of caring for people with diabetes is Therapeutic Education (TE). TE is a teaching process for training patients so that they can self-manage their care plan. Alongside traditional methods of providing educational content, there are now alternative forms of delivery thanks to the implementation of advanced Information Technologies systems such as conversational agents (CAs). In this context, we present the AIDA project: an ensemble of two different CAs intended to provide a TE tool for people with diabetes. The Artificial Intelligence Diabetes Assistant (AIDA) consists of a text-based chatbot and a speech-based dialog system. Their content has been created and validated by a scientific board. AIDA Chatbot—the text-based agent—provides a broad spectrum of information about diabetes, while AIDA Cookbot—the voice-based agent—presents recipes compliant with a diabetic patient’s diet. We provide a thorough description of the development process for both agents, the technology employed and their usage by the general public. AIDA Chatbot and AIDA Cookbot are freely available and they represent the first example of conversational agents in Italian to support diabetes patients, clinicians and caregivers.

A Survey on Spoken Language Understanding: Recent Advances and New Frontiers

Spoken Language Understanding (SLU) aims to extract the semantics frame of user queries, which is a core component in a task-oriented dialog system. With the burst of deep neural networks and the evolution of pre-trained language models, the research of SLU has obtained significant breakthroughs. However, there remains a lack of a comprehensive survey summarizing existing approaches and recent trends, which motivated the work presented in this article. In this paper, we survey recent advances and new frontiers in SLU. Specifically, we give a thorough review of this research field, covering different aspects including (1) new taxonomy: we provide a new perspective for SLU filed, including single model vs. joint model, implicit joint modeling vs. explicit joint modeling in joint model, non pre-trained paradigm vs. pretrained paradigm; (2) new frontiers: some emerging areas in complex SLU as well as the corresponding challenges; (3) abundant open-source resources: to help the community, we have collected, organized the related papers, baseline projects and leaderboard on a public website where SLU researchers could directly access to the recent progress. We hope that this survey can shed a light on future research in SLU field.