Published: June 21, 2024
Language: Английский
JMIR Cancer, Journal Year: 2025, Volume and Issue: 11, P. e66633 - e66633
Published: Feb. 18, 2025
This Viewpoint proposes a robust framework for developing medical chatbot dedicated to radiotherapy education, emphasizing accuracy, reliability, privacy, ethics, and future innovations. By analyzing existing research, the evaluates performance identifies challenges such as content bias, system integration. The findings highlight opportunities advancements in natural language processing, personalized learning, immersive technologies. When designed with focus on ethical standards large model–based chatbots could significantly impact education health care delivery, positioning them valuable tools developments globally.
Language: Английский
Citations
3
Algorithms, Journal Year: 2025, Volume and Issue: 18(3), P. 156 - 156
Published: March 9, 2025
Medical decision-making is increasingly integrating quantum computing (QC) and machine learning (ML) to analyze complex datasets, improve diagnostics, enable personalized treatments. While QC holds the potential accelerate optimization, drug discovery, genomic analysis as hardware capabilities advance, current implementations remain limited compared classical in many practical applications. Meanwhile, ML has already demonstrated significant success medical imaging, predictive modeling, decision support. Their convergence, particularly through (QML), presents opportunities for future advancements processing high-dimensional healthcare data improving clinical outcomes. This review examines foundational concepts, key applications, challenges of these technologies healthcare, explores their synergy solving problems, outlines directions quantum-enhanced decision-making.
Language: Английский
Citations
2
International Journal of Human-Computer Interaction, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 19
Published: Jan. 8, 2025
The advancement of artificial intelligence technology has enabled chatbots to mimic human conversations the point being nearly indistinguishable from humans. This study investigates impact chatbot proactivity on user experience, considering task types and characteristics. Experiments were conducted using proactive non-proactive for business-related non-business-related tasks, evaluating characteristics (age, gender, AI experience level, education personality traits) (perceived usefulness, perceived ease use, satisfaction, trust). results showed that positively evaluated in terms trust, but no significant difference was found use compared chatbots. Additionally, traits (extraversion, agreeableness) significantly influenced experience. highlights critical role providing satisfying interaction experiences emphasizes importance designing
Language: Английский
Citations
1
Computer Science Review, Journal Year: 2025, Volume and Issue: 56, P. 100725 - 100725
Published: Feb. 6, 2025
Language: Английский
Citations
1
Journal of Radiation Research and Applied Sciences, Journal Year: 2025, Volume and Issue: 18(2), P. 101353 - 101353
Published: Feb. 13, 2025
Language: Английский
Citations
1
International Journal of Human-Computer Interaction, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 12
Published: Aug. 16, 2024
In many nations, demand for mental health services currently outstrips supply, especially in the area of talk-based psychological interventions. Within this context, chatbots (software applications designed to simulate conversations with human users) are increasingly explored as potential adjuncts traditional healthcare service delivery a view improving accessibility and reducing waiting times. However, effectiveness acceptability such remains under-researched. This study evaluates professionals' perceptions Pi, relational Artificial Intelligence (AI) chatbot, early stages psychotherapeutic process (problem exploration). We asked 63 therapists assess therapy transcripts between client Pi (human-AI) versus clients (human-human). Therapists were unable reliably discriminate human-AI human-human transcripts. accurate only 53.9% time, no better than chance, rated higher quality on average. has potentially profound implications treatment problems, adding tentative support use AI providing initial assistance mild moderate issues, when access is constrained.
Language: Английский
Citations
8
Dental Traumatology, Journal Year: 2024, Volume and Issue: unknown
Published: Nov. 22, 2024
ABSTRACT Background/Aim Artificial intelligence (AI) chatbots have become increasingly prevalent in recent years as potential sources of online healthcare information for patients when making medical/dental decisions. This study assessed the readability, quality, and accuracy responses provided by three AI to questions related traumatic dental injuries (TDIs), either retrieved from popular question‐answer sites or manually created based on hypothetical case scenarios. Materials Methods A total 59 injury queries were directed at ChatGPT 3.5, 4.0, Google Gemini. Readability was evaluated using Flesch Reading Ease (FRE) Flesch–Kincaid Grade Level (FKGL) scores. To assess response quality accuracy, DISCERN tool, Global Quality Score (GQS), misinformation scores used. The understandability actionability analyzed Patient Education Assessment Tool Printed (PEMAT‐P) tool. Statistical analysis included Kruskal–Wallis with Dunn's post hoc test non‐normal variables, one‐way ANOVA Tukey's normal variables ( p < 0.05). Results mean FKGL FRE Gemini 11.2 49.25, 11.8 46.42, 10.1 51.91, respectively, indicating that difficult read required a college‐level reading ability. 3.5 had lowest PEMAT‐P among 0.001). 4.0 rated higher (GQS score 5) compared Conclusions In this study, although widely used, some misleading inaccurate about TDIs. contrast, generated more accurate comprehensive answers, them reliable auxiliary sources. However, complex issues like TDIs, no chatbot can replace dentist diagnosis, treatment, follow‐up care.
Language: Английский
Citations
5
International Journal of Human-Computer Interaction, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 10
Published: Nov. 15, 2024
With the advancement of artificial intelligence technology, generative AI chatbots (GAIC) such as ChatGPT, are integrating into various aspects society and quietly changing people's lives. However, although there is a plethora research concerning social media usage addiction, discussions about remain far from adequate. Investigating use potential technology addiction to (TAGAIC) holds both theoretical practical significance. Drawing on attachment theory, this study aims investigate formative factors in TAGAIC. A structural equation model was used analyze data collected questionnaire survey 364 GAIC users. Results reveal that positively influenced by emotional but not functional attachment. Besides, perceived anthropomorphism empathy have positive effect attachment, while system quality information Limitations were discussed.
Language: Английский
Citations
4
International Journal of Human-Computer Interaction, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 24
Published: March 24, 2025
Language: Английский
Citations
0
Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, Journal Year: 2025, Volume and Issue: 15(2)
Published: April 9, 2025
ABSTRACT This paper reviews benchmarking methods for evaluating large language models (LLMs) in healthcare settings. It highlights the importance of rigorous to ensure LLMs' safety, accuracy, and effectiveness clinical applications. The review also discusses challenges developing standardized benchmarks metrics tailored healthcare‐specific tasks such as medical text generation, disease diagnosis, patient management. Ethical considerations, including privacy, data security, bias, are addressed, underscoring need multidisciplinary collaboration establish robust frameworks that facilitate reliable ethical use healthcare. Evaluation LLMs remains challenging due lack comprehensive datasets. Key concerns include model better explainability, all which impact overall trustworthiness
Language: Английский
Citations
0