Evaluation Metrics for Health Chatbots: A Delphi Study

Developing Effective Frameworks for Large Language Model-Based Medical Chatbots: Insights From Radiotherapy Education With ChatGPT

This Viewpoint proposes a robust framework for developing a medical chatbot dedicated to radiotherapy education, emphasizing accuracy, reliability, privacy, ethics, and future innovations. By analyzing existing research, the framework evaluates chatbot performance and identifies challenges such as content accuracy, bias, and system integration. The findings highlight opportunities for advancements in natural language processing, personalized learning, and immersive technologies. When designed with a focus on ethical standards and reliability, large language model-based chatbots could significantly impact radiotherapy education and health care delivery, positioning them as valuable tools for future developments in medical education globally.

Ethical Considerations in Human-Centered AI: Advancing Oncology Chatbots Through Large Language Models

The integration of chatbots in oncology underscores the pressing need for human-centered artificial intelligence (AI) that addresses patient and family concerns with empathy and precision. Human-centered AI emphasizes ethical principles, empathy, and user-centric approaches, ensuring technology aligns with human values and needs. This review critically examines the ethical implications of using large language models (LLMs) like GPT-3 and GPT-4 (OpenAI) in oncology chatbots. It examines how these models replicate human-like language patterns, impacting the design of ethical AI systems. The paper identifies key strategies for ethically developing oncology chatbots, focusing on potential biases arising from extensive datasets and neural networks. Specific datasets, such as those sourced from predominantly Western medical literature and patient interactions, may introduce biases by overrepresenting certain demographic groups. Moreover, the training methodologies of LLMs, including fine-tuning processes, can exacerbate these biases, leading to outputs that may disproportionately favor affluent or Western populations while neglecting marginalized communities. By providing examples of biased outputs in oncology chatbots, the review highlights the ethical challenges LLMs present and the need for mitigation strategies. The study emphasizes integrating human-centric values into AI to mitigate these biases, ultimately advocating for the development of oncology chatbots that are aligned with ethical principles and capable of serving diverse patient populations equitably.

Framework for Guiding the Development of High-Quality Conversational Agents in Healthcare

Evaluating conversational agents (CAs) that are supposed to be applied in healthcare settings and ensuring their quality is essential to avoid patient harm and ensure efficacy of the CA-delivered intervention. However, a guideline for a standardized quality assessment of health CAs is still missing. The objective of this work is to describe a framework that provides guidance for development and evaluation of health CAs. In previous work, consensus on categories for evaluating health CAs has been found. In this work, we identify concrete metrics, heuristics, and checklists for these evaluation categories to form a framework. We focus on a specific type of health CA, namely rule-based systems that are based on written input and output, have a simple personality without any kind of embodiment. First, we identified relevant metrics, heuristics, and checklists to be linked to the evaluation categories through a literature search. Second, five experts judged the metrics regarding their relevance to be considered within evaluation and development of health CAs. The final framework considers nine aspects from a general perspective, five aspects from a response understanding perspective, one aspect from a response generation perspective, and three aspects from an aesthetics perspective. Existing tools and heuristics specifically designed for evaluating CAs were linked to these evaluation aspects (e.g., Bot usability scale, design heuristics for CAs); tools related to mHealth evaluation were adapted when necessary (e.g., aspects from the ISO technical specification for mHealth Apps). The resulting framework comprises aspects to be considered not only as part of a system evaluation, but already during the development. In particular, aspects related to accessibility or security have to be addressed in the design phase (e.g., which input and output options are provided to ensure accessibility?) and have to be verified after the implementation phase. As a next step, transfer of the framework to other types of health CAs has to be studied. The framework has to be validated by applying it during health CA design and development.

Developing a Technical-Oriented Taxonomy to Define Archetypes of Conversational Agents in Health Care: Literature Review and Cluster Analysis

BACKGROUND

The evolution of artificial intelligence and natural language processing generates new opportunities for conversational agents (CAs) that communicate and interact with individuals. In the health domain, CAs became popular as they allow for simulating the real-life experience in a health care setting, which is the conversation with a physician. However, it is still unclear which technical archetypes of health CAs can be distinguished. Such technical archetypes are required, among other things, for harmonizing evaluation metrics or describing the landscape of health CAs.

OBJECTIVE

The objective of this work was to develop a technical-oriented taxonomy for health CAs and characterize archetypes of health CAs based on their technical characteristics.

METHODS

We developed a taxonomy of technical characteristics for health CAs based on scientific literature and empirical data and by applying a taxonomy development framework. To demonstrate the applicability of the taxonomy, we analyzed the landscape of health CAs of the last years based on a literature review. To form technical design archetypes of health CAs, we applied a k-means clustering method.

RESULTS

Our taxonomy comprises 18 unique dimensions corresponding to 4 perspectives of technical characteristics (setting, data processing, interaction, and agent appearance). Each dimension consists of 2 to 5 characteristics. The taxonomy was validated based on 173 unique health CAs that were identified out of 1671 initially retrieved publications. The 173 CAs were clustered into 4 distinctive archetypes: a text-based ad hoc supporter; a multilingual, hybrid ad hoc supporter; a hybrid, single-language temporary advisor; and, finally, an embodied temporary advisor, rule based with hybrid input and output options.

CONCLUSIONS

From the cluster analysis, we learned that the time dimension is important from a technical perspective to distinguish health CA archetypes. Moreover, we were able to identify additional distinctive, dominant characteristics that are relevant when evaluating health-related CAs (eg, input and output options or the complexity of the CA personality). Our archetypes reflect the current landscape of health CAs, which is characterized by rule based, simple systems in terms of CA personality and interaction. With an increase in research interest in this field, we expect that more complex systems will arise. The archetype-building process should be repeated after some time to check whether new design archetypes emerge.

An Explainable Knowledge-Based System Using Subjective Preferences and Objective Data for Ranking Decision Alternatives

BACKGROUND

 Allergy is a hypersensitive reaction that occurs when the allergen reacts with the immune system. The prevalence and severity of the allergies are uprising in South Asian countries. Allergy often occurs in combinations which becomes difficult for physicians to diagnose.

OBJECTIVES

 This work aims to develop a decision-making model which aids physicians in diagnosing allergy comorbidities. The model intends to not only provide rational decisions, but also explainable knowledge about all alternatives.

METHODS

 The allergy data gathered from real-time sources contain a smaller number of samples for comorbidities. Decision-making model applies three sampling strategies, namely, ideal, single, and complete, to balance the data. Bayes theorem-based probabilistic approaches are used to extract knowledge from the balanced data. Preference weights for attributes with respect to alternatives are gathered from a group of domain-experts affiliated to different allergy testing centers. The weights are combined with objective knowledge to assign confidence values to alternatives. The system provides these values along with explanations to aid decision-makers in choosing an optimal decision.

RESULTS

 Metrics of explainability and user satisfaction are used to evaluate the effectiveness of the system in real-time diagnosis. Fleiss' Kappa statistic is 0.48, and hence the diagnosis of experts is said to be in moderate agreement. The decision-making model provides a maximum of 10 suitable and relevant pieces of evidence to explain a decision alternative. Clinicians have improved their diagnostic performance by 3% after using CDSS (77.93%) with a decrease in 20% of time taken.

CONCLUSION

 The performance of less-experienced clinicians has improved with the support of an explainable decision-making model. The code for the framework with all intermediate results is available at https://github.com/kavya6697/Allergy-PT.git.

Human-Computer Interaction in Digital Mental Health

Human-computer interaction (HCI) has contributed to the design and development of some efficient, user-friendly, cost-effective, and adaptable digital mental health solutions. But HCI has not been well-combined into technological developments resulting in quality and safety concerns. Digital platforms and artificial intelligence (AI) have a good potential to improve prediction, identification, coordination, and treatment by mental health care and suicide prevention services. AI is driving web-based and smartphone apps; mostly it is used for self-help and guided cognitive behavioral therapy (CBT) for anxiety and depression. Interactive AI may help real-time screening and treatment in outdated, strained or lacking mental healthcare systems. The barriers for using AI in mental healthcare include accessibility, efficacy, reliability, usability, safety, security, ethics, suitable education and training, and socio-cultural adaptability. Apps, real-time machine learning algorithms, immersive technologies, and digital phenotyping are notable prospects. Generally, there is a need for faster and better human factors in combination with machine interaction and automation, higher levels of effectiveness evaluation and the application of blended, hybrid or stepped care in an adjunct approach. HCI modeling may assist in the design and development of usable applications, and to effectively recognize, acknowledge, and address the inequities of mental health care and suicide prevention and assist in the digital therapeutic alliance.