Assessing the Potential Risks of Digital Therapeutics (DTX): The DTX Risk Assessment Canvas

Co-Creation in the Development of Digital Therapeutics: A Narrative Review

Digital therapeutics (DTx) are transforming healthcare delivery through personalised, evidence-based interventions that offer a cost-effective approach to health management. However, their widespread adoption faces significant barriers including privacy concerns, usability issues, and integration challenges within healthcare systems. This review assesses the current evidence on DTx, with a particular focus on the role of co-creation in enhancing design and usability. A narrative review was conducted to identify studies exploring co-creation in DTx development. Three studies were selected for in-depth analysis, demonstrating that co-creation processes significantly improve the usability and effectiveness of DTx interventions. Findings underscore challenges in DTx implementation, including complex regulatory processes, digital inequality, high development costs, and difficulties in integrating with existing healthcare systems. Despite the existence of discrete examples of co-creation in DTx and its acknowledged value in the healthcare domain, systematic research in this field remains markedly limited. Future studies should prioritise establishing best practises for co-creation, with particular emphasis on methods to enhance data privacy and security, standardisation protocols, and patient engagement strategies to optimise DTx adoption and effectiveness. This review contributes to the growing body of literature on DTx by highlighting the potential of co-creation while also identifying critical areas for future research.

The ethical aspects of integrating sentiment and emotion analysis in chatbots for depression intervention

Introduction

Digital health interventions specifically those realized as chatbots are increasingly available for mental health. They include technologies based on artificial intelligence that assess user's sentiment and emotions for the purpose of responding in an empathetic way, or for treatment purposes, e.g. for analyzing the expressed emotions and suggesting interventions.

Methods

In this paper, we study the ethical dimensions of integrating these technologies in chatbots for depression intervention using the digital ethics canvas and the DTx Risk Assessment Canvas.

Results

As result, we identified some specific risks associated with the integration of sentiment and emotion analysis methods into these systems related to the difficulty to recognize correctly the expressed sentiment or emotion from statements of individuals with depressive symptoms and the appropriate system reaction including risk detection. Depending on the realization of the sentiment or emotion analysis, which might be dictionary-based or machine-learning based, additional risks occur from biased training data or misinterpretations.

Discussion

While technology decisions during system development can be made carefully depending on the use case, other ethical risks cannot be prevented on a technical level, but by carefully integrating such chatbots into the care process allowing for supervision by health professionals. We conclude that a careful reflection is needed when integrating sentiment and emotion analysis into chatbots for depression intervention. Balancing risk factors is key to leveraging technology in mental health in a way that enhances, rather than diminishes, user autonomy and agency.

Selected Papers from the pHealth 2022 Conference, Oslo, Norway, 8-10 November 2022

This Special Issue of the Journal of Personalized Medicine presents extended versions of selected contributions to pHealth 2022, the 19th International Conference on Wearable Micro and Nano Technologies for Personalized Health, held on 8-10 November 2022 in Oslo, Norway [...].

Transformer Models in Healthcare: A Survey and Thematic Analysis of Potentials, Shortcomings and Risks

Large Language Models (LLMs) such as General Pretrained Transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT), which use transformer model architectures, have significantly advanced artificial intelligence and natural language processing. Recognized for their ability to capture associative relationships between words based on shared context, these models are poised to transform healthcare by improving diagnostic accuracy, tailoring treatment plans, and predicting patient outcomes. However, there are multiple risks and potentially unintended consequences associated with their use in healthcare applications. This study, conducted with 28 participants using a qualitative approach, explores the benefits, shortcomings, and risks of using transformer models in healthcare. It analyses responses to seven open-ended questions using a simplified thematic analysis. Our research reveals seven benefits, including improved operational efficiency, optimized processes and refined clinical documentation. Despite these benefits, there are significant concerns about the introduction of bias, auditability issues and privacy risks. Challenges include the need for specialized expertise, the emergence of ethical dilemmas and the potential reduction in the human element of patient care. For the medical profession, risks include the impact on employment, changes in the patient-doctor dynamic, and the need for extensive training in both system operation and data interpretation.