Clinical-chatbot AHP evaluation based on "quality in use" of ISO/IEC 25010.

BACKGROUND: Conversational agents are currently a valid alternative to humans in first-level interviews with users who need information, even in-depth, about services or products. In application domains such as health care, this technology can become pervasive only if the perceived "quality in use" is appropriate. How to measure chatbot quality is an open question. The international standard ISO/IEC 25010 proposes a set of characteristics (effectiveness, efficiency, satisfaction, freedom from risk, and context coverage) to be considered when the "quality in use" of a software system has to be measured. BASIC PROCEDURE: This study proposes a clinical chatbot comparison method based on quality. The proposed approach is based on Analytic Hierarchy Process methodology (AHP). FINDINGS: Our contribution is twofold. First, we propose a set of measures for each characteristic of ISO/IEC 25010 according to three classes of functionality: providing information, providing prescriptions and process management. Moreover a quantitative method is proposed for making homogeneous the pairwise weights when the AHP is used for the "quality-in-use" comparison. As a case study, a comparison of two versions of a chatbot was performed. CONCLUSIONS: The results show that the proposed approach provides an effective reference base for performing quality comparisons of medical chatbots compliant with the ISO/IEC 25010 standard.

A systematic mapping study on machine learning techniques for the prediction of CRISPR/Cas9 sgRNA target cleavage.

CRISPR/Cas9 technology has greatly accelerated genome engineering research. The CRISPR/Cas9 complex, a bacterial immune response system, is widely adopted for RNA-driven targeted genome editing. The systematic mapping study presented in this paper examines the literature on machine learning (ML) techniques employed in the prediction of CRISPR/Cas9 sgRNA on/off-target cleavage, focusing on improving support in sgRNA design activities and identifying areas currently being researched. This area of research has greatly expanded recently, and we found it appropriate to work on a Systematic Mapping Study (SMS), an investigation that has proven to be an effective secondary study method. Unlike a classic review, in an SMS, no comparison of methods or results is made, while this task can instead be the subject of a systematic literature review that chooses one theme among those highlighted in this SMS. The study is illustrated in this paper. To the best of the authors' knowledge, no other SMS studies have been published on this topic. Fifty-seven papers published in the period 2017-2022 (April, 30) were analyzed. This study reveals that the most widely used ML model is the convolutional neural network (CNN), followed by the feedforward neural network (FNN), while the use of other models is marginal. Other interesting information has emerged, such as the wide availability of both open code and platforms dedicated to supporting the activity of researchers or the fact that there is a clear prevalence of public funds that finance research on this topic.

The ARAMIS project: a concept robot and technical design.

OBJECTIVE: To describe the ARAMIS (Automatic Recovery Arm Motility Integrated System) project, a concept robot applicable in the neuro-rehabilitation of the paretic upper limb after stroke. Methods, results and conclusion: The rationale and engineering of a state-of-the-art, hardware/software integrated robot system, its mechanics, ergonomics, electric/electronics features providing control, safety and suitability of use are described. An ARAMIS prototype has been built and is now available for clinical tests. It allows the therapist to design neuro-rehabilitative (synchronous or asynchronous) training protocols in which sample exercises are generated by a single exoskeleton (operated by the patient's unaffected arm or by the therapist's arm) and mirrored in real-time or offline by the exoskeleton supporting the paretic arm.