Evaluating user satisfaction and experiences with selected AI chatbot healthcare applications available on the Google Play Store concludes that AI chatbots are best positioned to complement healthcare professionals, rather than totally replace them.
Artificial intelligence (AI) and chatbots are expected to play an increasingly important role in patient management systems worldwide. These technologies have the potential to support preliminary triage, reduce waiting times, and alleviate the administrative burden placed on healthcare professionals. At the same time, their application raises substantial challenges related to data protection, ethical accountability, clinical responsibility, and professional practice. The purpose of this study is to present the opportunities and limitations of chatbot applications in healthcare and to illustrate their regional relevance in Hungary through the example of Northern Hungary. Based on a structured literature review and secondary data analysis, the findings indicate that chatbot integration may contribute to enhanced system efficiency and reduced inequalities in access to outpatient care, provided that implementation is accompanied by appropriate regulatory, ethical, and institutional safeguards.
Andrea Kelen, Beatrix Faragó· Észak-magyarországi Stratégi...· 0 citations
Multilingual AI chatbots demonstrate a boost in healthcare efficiency, a reduction in language barriers, and the promotion of health equity, but exhibit challenges regarding validation, workflow integration, and evaluation standards, along with ethical issues such as privacy and bias.
R. Sharanesha, Deepti Virupakshappa, A. Abushanan et al.· Informatics· 0 citations
The provision of medicines information (MI) services requires interpretation and clinical judgement of complex scenarios by pharmacists. To date, few studies have assessed the performance of artificial intelligence (AI) chatbots to assist pharmacists providing MI advice.
To evaluate the performance and risk associated with two AI chatbots (Microsoft Copilot and Google Gemini) to answer medicines‐related questions.
A sample of 20 questions answered by the local MI service in November 2023 was entered in the two chatbot applications in January 2024 (round 1) and May 2024 (round 2). All questions were preceded with the prompt ‘I'm a pharmacist’. Chatbot responses were evaluated by comparing with a reference answer given by the MI service using a consensus process in the domains of content, patient management, risk of patient harm, and follow up review. Ethical approval was granted by the Canterbury District Health Board Research Office (Reference no: 20311) and the study conforms with the Declaration of Helsinki.
For the 20 questions answered by both chatbots, few of the round 1 responses (
n
= 4 for Copilot and
n
= 2 for Gemini) were considered complete and with adequate information to commence patient management with no risk of harm. Most were incomplete (
n
= 13 for Copilot and
n
= 15 for Gemini) regarding content, but none were high risk of causing harm. In round 1, four responses from Copilot and eight from Gemini were flagged for follow up review. There was no significant difference in performance between chatbots in round 1 (p = 0.68) or between rounds 1 and 2 (Copilot p = 0.25 and Gemini p > 0.99).
Our study results demonstrated the chatbots' responses were typically suboptimal; albeit, a significant minority prompted a follow up to review the chatbot response.
Duncan Yorkston, Tracey Borrie, Paul K L Chin· Journal of Pharmacy Practice...· 0 citations
Artificial Intelligence (AI) has significantly transformed customer service by enabling organizations to provide instant, personalized, and cost-effective support through AI-powered chatbots. Businesses across industries, including banking, healthcare, retail, education, and e-commerce, increasingly rely on chatbots to improve customer engagement and operational efficiency. However, while chatbots offer numerous benefits, their impact on customer satisfaction depends on factors such as response quality, personalization, ease of use, reliability, and the ability to resolve customer issues effectively. This analytical research paper examines the relationship between AI-powered chatbots and customer satisfaction by analysing existing theoretical concepts, customer expectations, and practical business applications. The paper concludes that AI-powered chatbots positively influence customer satisfaction when they are accurate, responsive, user-friendly, and supported by human assistance for complex issues. Organizations should therefore adopt a hybrid customer service approach that combines AI efficiency with human empathy.
Bijoy Karmakar, Richa Handa, Sharad Bajpai· International Journal For Mu...· 0 citations
The integration of artificial intelligence (AI) in healthcare has increased rapidly, with large language model-based chatbots emerging as potential tools for education and clinical support. However, their performance in complex medical domains such as sedation and general anesthesia remains underexplored. This study aimed to evaluate the accuracy of AI-supported chatbot models (ChatGPT 4.0 Mini, Gemini 1.5 Pro, and Claude 3 Sonnet) in comparison to human experts (Anesthesiologists, Pediatric Dentists, Oral and Maxillofacial Surgeons) within the context of sedation and general anesthesia.
This descriptive and comparative cross-sectional study utilized a 26-item true/false questionnaire based on ASA, AAP, ADA, and AAPD guidelines. The questionnaire was administered to three chatbots and 72 human participants (24 per specialty). For chatbot evaluation, a zero-shot prompting technique with a uniform command (“Is this statement true or false?”) was applied in separate sessions after clearing the cache to ensure standardization. Responses were coded as correct/incorrect by two independent pediatric dentists. Human participants completed the same questionnaire via Google Forms. Descriptive statistics were calculated. Normality of data was assessed using the Shapiro-Wilk test. Non-parametric data were compared using the Kruskal-Wallis test followed by Bonferroni-adjusted post hoc tests. Categorical variables were analyzed using the Pearson Chi-square test. Statistical analyses were performed using IBM SPSS v27.
Significant differences were found among all groups ($
p
< 0.001$). In terms of text-based guideline retrieval, Claude 3 Sonnet (92.8%) and Gemini 1.5 Pro (91.7%) demonstrated high factual accuracy, while anesthesiologists achieved the highest performance among human clinicians (87.0%). While the chatbot models showed high proficiency in data retrieval for “General Information” and “Postoperative Monitoring,” anesthesiologists significantly outperformed the AI models in “Patient Evaluation and Preparation” (91.7%), highlighting a critical gap in AI’s clinical reasoning compared to human expertise. Notable knowledge gaps were identified among oral surgeons regarding pharmacological reversal agents.
Claude and Gemini chatbots exhibit high factual accuracy in retrieving guideline-based pediatric sedation theory, suggesting potential exclusively as supplementary informational resources. However, anesthesiologists’ superior performance in pre-operative assessment underscores the irreplaceable role of clinical judgment. AI integration should therefore follow a synergistic model, positioning language models strictly as adjunct tools under mandatory human oversight.
Dilara Dinc, Aslıhan Ozbilgen· BMC Medical Informatics and...· 0 citations
The increasing demand for accessible healthcare services, coupled with the shortage of medical professionals and geographical barriers, highlights the need for intelligent digital healthcare solutions. Traditional medical chatbots are largely limited to text-based interactions, lacking the ability to process multimodal inputs such as speech and medical images, thereby restricting their effectiveness in real-world scenarios. This paper presents DawAI, a multimodal AI-powered virtual medical assistant designed to simulate real-time doctor– patient interactions. The system integrates advanced technologies including speech-to-text conversion for interpreting spoken symptoms, image-based analysis for visual medical inputs, and large language models for generating contextaware medical responses. Additionally, a text-to-speech module enables the system to deliver responses in a natural, human-like voice, enhancing user accessibility and interaction. DawAI operates through a unified architecture that processes voice and image inputs, performs multimodal reasoning, and generates informative, empathetic responses within seconds. A structured dataset comprising symptom descriptions, severity levels, and precautionary measures supports the system’s reasoning capability, ensuring coherent and medically relevant outputs. Experimental evaluation demonstrates that the system provides consistent and context-sensitive responses while maintaining real-time performance. By addressing the limitations of existing healthcare chatbots, DawAI offers a scalable, accessible, and user-friendly solution for preliminary medical consultation, particularly benefiting users in remote and resource-constrained environments.
Dr. Abdul Khadeer, Mohammed Zubair Ahmed· International Journal of Eng...· 0 citations