Conversational agents are software programs made to mimic human talk using text or voice. In healthcare, they mainly help patients manage chronic diseases like diabetes, asthma, cancer, mental health issues, and COVID-19. These agents give advice, reminders, and support for healthy habits without needing a person right away.
A review done in 2023 looked at 23 studies on automation methods for personalized healthcare using conversational agents. It found different types of systems:
Out of 23 studies, seven used rule-based, eleven used retrieval-based, five used AI models, and six used affective computing. This shows more interest in emotional response in healthcare agents, but personalizing and flexible conversations still need work.
Affective computing means technology that can notice, understand, and react to human feelings. For healthcare conversational agents, this means seeing emotions like stress or anxiety when patients talk and responding in helpful ways emotionally, not just medically.
When agents connect emotionally with patients, the relationship improves. This can help patients follow care plans better and keep using the system. Emotional response in agents helps people manage chronic diseases and practice healthy habits. For example, in mental health support, agents that detect distress can give more caring and fitting responses.
Important features of these systems include:
However, many current agents don’t do these well. They often have limited ways to change conversation and basic personalization that uses fixed rules instead of truly understanding the patient’s situation.
Most healthcare conversational agents today use fixed or database-driven responses. Only a few use AI that can adapt. This makes conversations seem robotic and not very personal, which fails patients with complex needs.
Also, full user profiles that combine medical history, emotions, and social background are hard to build safely and correctly. Without these, real personalization is tough.
Since emotional information is private, keeping it safe is very important. Healthcare workers worry about data leaks or misuse. US laws like HIPAA set strict rules about handling patient data. AI systems that read emotions must follow ethical rules to keep patient trust and handle sensitive health info responsibly.
Understanding feelings from voice or text is tricky. If the system gets it wrong, it can give bad responses that confuse or upset users. It’s hard to make emotion detection work well for all kinds of people and languages in the US.
Many healthcare offices find it hard to connect conversational agents with their current software, like electronic health records and management tools. Smooth connection is needed so agents can access patient data, update records, and help without breaking workflows.
Some AI research looks at social companionship, which means agents build emotional connections with users. This helps users feel closer to the system and interact more often.
Researchers like Rijul Chaturvedi, Ronnie Das, and Yogesh K. Dwivedi studied how emotional AI helps create social companionship. They found that emotional bonds can improve health results by making users more engaged and following care plans better.
Still, questions remain about ethical AI design, privacy, and when to use AI versus human help. Healthcare managers face tough choices in how much to trust emotionally smart agents in sensitive situations.
Healthcare offices in the US deal with many phone calls, scheduling, patient questions, and billing. AI phone automation can help handle these tasks better.
Simbo AI is one company that offers AI phone systems that answer calls in a way that feels human. It uses some affective computing to change responses depending on caller tone or purpose. This helps patients feel satisfied and lowers missed calls.
Simbo AI shows how AI tools help run healthcare offices better by fixing common problems like drop calls and unclear patient messages. Their system reacts to caller needs, such as noticing urgency or stress.
Improving affective computing in healthcare agents depends on more research:
US healthcare must follow rules like HIPAA that protect patient data, especially emotional health info. AI systems need strong safety measures when handling this data.
The US has a mix of cultures and languages. Agents must understand and reply properly to different dialects, languages, and traditions. Making sure all patients benefit equally is a big challenge.
Many healthcare groups use electronic health records now, but linking AI agents with them needs good technology and standards to work well. IT managers need to check if systems fit and can grow with their needs.
AI systems can be expensive upfront and may cost money to maintain. Small clinics might have trouble paying for them. Services like Simbo AI offer options that fit big and small clinics by improving communication at lower costs.
Healthcare leaders who keep up with these changes can choose better AI tools that improve patient care and office workflow.
Adding affective computing to healthcare agents might make them more human-like and emotionally aware to meet patient needs. As AI improves, US healthcare should think carefully about these tech tools while protecting privacy and following ethical and clinical rules.
Conversational agents (CAs) are automated systems designed to interact with users through human-like dialogue. They provide personalized healthcare interventions by delivering tailored advice, supporting self-management of diseases, and promoting healthy habits, thus improving health outcomes sustainably.
Healthcare CAs primarily assist patients dealing with diabetes, mental health issues, cancer, asthma, COVID-19, and other chronic conditions. They also focus on enhancing healthy behaviors to prevent disease onset or progression.
Key features include system flexibility in conversations, personalization of interaction based on user data, and affective characteristics such as recognizing and responding to user emotions to make interactions more engaging.
Development techniques include rule-based models (used in 7 studies), retrieval-based techniques for content delivery (11 studies), AI models (5 studies), and integration of affective computing (6 studies) to enhance personalization and emotional responsiveness.
Dialogue structures and personalization remain limited due to constrained adaptability to diverse user needs and contexts. Many systems still lack holistic user modeling and dynamic response generation, which restricts their ability to conduct truly human-like conversations.
Affective computing enables CAs to detect and respond to user emotions, improving engagement and adherence by providing empathetic, context-aware interactions that mimic human empathy and support user emotional needs during healthcare dialogues.
Generative AI can enable more natural, flexible, and context-aware conversations by producing human-like responses dynamically, supporting deeper personalization and better user engagement while addressing challenges related to safety and reliability.
A scoping review following the PRISMA Extension for Scoping Reviews was conducted, with systematic searches in Web of Science, PubMed, Scopus, and IEEE databases. Screening and characterization of relevant studies focused on personalized automated CAs within healthcare.
The research targets designers and developers of healthcare CAs, computational scientists, behavioral scientists, and biomedical engineers aiming to develop and improve personalized healthcare interventions using conversational agents.
Future research should integrate holistic user description methods and focus on safely implementing generative AI models and affective computing to unlock more adaptive, empathetic, and personalized healthcare conversations with users.
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