Search Results (19)

Background/Objectives: Penile cancer carries a disproportionate burden of stigma and delayed presentation, with affected men increasingly turning to artificial intelligence (AI) chatbots as an anonymous information source. This study evaluated the quality, readability, understandability, actionability and clinical accuracy of AI chatbot responses to standardised penile cancer patient queries across six publicly available platforms. Methods: Fourteen standardised questions were submitted to ChatGPT-4o, Gemini, Perplexity, Microsoft Copilot, Claude, and DeepSeek, generating 84 responses. The responses were evaluated using DISCERN (information quality, scored 16–80), Patient Education Materials Assessment Tool for Printable Materials (PEMAT-P) (understandability and actionability, scored 0–100%), and Flesch–Kincaid grade level (reading complexity, recommended threshold ≤Grade 8). Between-platform and between-domain comparisons used the Kruskal–Wallis test with Dunn’s post hoc analysis. Clinical accuracy was assessed against the 2026 European Association of Urology-American Society of Clinical Oncology (EAU-ASCO) Collaborative Guidelines on Penile Cancer using a three-point ordinal scale across 10 guideline-scorable questions (maximum 20 points per platform); the four questions not addressed by the guidelines were scored separately for factual accuracy against authoritative external evidence. Results: No significant between-platform differences were identified for DISCERN (p = 0.796) or PEMAT-P understandability (p = 0.147). All platforms exceeded the 70% understandability adequacy threshold. Perplexity and Copilot demonstrated significantly higher actionability than all other platforms (100% vs. 75%, p < 0.001). No platforms achieved the recommended Grade 8 reading threshold, with Claude generating significantly more complex responses than ChatGPT-4o and Perplexity (grade 11.4 vs. 8.65 and 8.60, p < 0.05). Significant variation was identified across clinical domains for DISCERN (p < 0.001), Flesch–Kincaid grade level (p < 0.001), and word count (p < 0.001), with treatment-related questions achieving the highest information quality but also generating the most complex responses. Clinical accuracy scores ranged from 13/20 (ChatGPT-4o) to 16/20 (Copilot). Two critical errors were identified: Claude and DeepSeek both recommended bleomycin-containing chemotherapy regimens, directly contradicting the EAU-ASCO Strong recommendation against bleomycin due to pulmonary toxicity risk. Responses to the four survivorship and quality-of-life questions were factually accurate against external evidence in 23 of 24 cases. Conclusions: AI chatbot responses to penile cancer patient queries are broadly understandable but consistently fail to meet recommended readability thresholds and provide limited actionable guidance. Two platforms recommended bleomycin-containing regimens against an EAU-ASCO Strong recommendation; no platforms achieved full guideline concordance. Urologists should counsel patients on the limitations of AI chatbots as a health information source. Full article

Background/Objectives: Artificial intelligence (AI), including large language models, chatbots, machine-learning systems, and hybrid tools, is increasingly used to support patient-facing eye-care communication. This systematic review evaluated the accuracy and effectiveness of AI-powered systems used for patient counseling, education, communication, referral/follow-up, and management support in optometry and related eye-care settings. Methods: A systematic review was conducted according to a predefined protocol and PRISMA 2020 reporting principles. Searches covered studies published from January 2020 to 31 May 2026 in PubMed, Embase, Web of Science, Cochrane Library, and IEEE Xplore. Eligible studies were original primary studies evaluating AI-supported tools for patient-facing counseling, education, question answering, treatment or medication guidance, triage, referral, follow-up, screening linked to management, or clinical decision support. Methodological quality was appraised using relevant JBI critical appraisal tools. Results: Thirty-nine studies were included in the qualitative synthesis. JBI appraisal indicated heterogeneous study designs and reporting quality, with common limitations related to simulated prompts or AI-output evaluations, variable comparators, and inconsistent outcome reporting. Evidence was dominated by patient-facing large language models and chatbot-based tools used for patient education, question answering, readability improvement, glaucoma and myopia counseling, diabetic retinopathy referral/follow-up, cataract education, oculoplastic and retinal-condition questions, and multilingual educational support. Study designs, AI models, prompting approaches, comparators, clinical topics, and outcome definitions varied widely, supporting narrative synthesis rather than quantitative pooling. Conclusions: AI-powered systems show potential as supervised adjunctive tools for eye-care counseling, education, and management-related communication. However, evidence remains heterogeneous and dependent on simulated prompts, AI-generated outputs, and model-based evaluations. Future research should prioritize standardized evaluation, real-patient validation, safety monitoring, readability control, and patient-centered outcomes before routine implementation in optometry and broader eye-care practice across diverse clinical settings. Full article

The increased use of technology-assisted distance counseling practices is one result of COVID’s impact on behavioral health, including in counselor education and the delivery of supervision. First, technology-assisted distance supervision needed for “real time” communication grew. Furthermore, there is an emergence of artificial intelligence (AI) technologies that have the potential to contribute to aspects of supervision; however, current evidence remains emerging, context-dependent, and at times mixed, warranting cautious interpretation of their effectiveness. The article offers an overview of using AI in clinical supervision, examines the benefits and potential concerns of AI from different perspectives, and considers the significance of using AI in counseling supervision. The role of AI is discussed as applied to counseling supervision including the use of AI tools, such as chatbots and reasoning AI, to detect and track sessions, note behavioral and emotional cues, aid/monitor communication and feedback, while also attending to ethical and legal consideration for its use. The article will report a range of benefits for supervisors and trainees using AI—for example, by enhancing data-driven supervision decisions, analyzing feedback trends, providing more efficient administrative monitoring, flexible/remote support, skill development, and promoting ethical decisions and self-reflection. Special attention is given to the challenges of using AI in supervision, including risks of undervaluing intuition and qualitative insights, potential for algorithms to reinforce systemic biases, risks of replacing human interaction, as well as non-compliance with HIPAA, FERPA, and ethical guidelines in data storage and privacy. The article will discuss privacy concerns, depersonalized feedback, and increased judgment-driven anxiety despite needed empathy when using AI as a tool for clinical supervision. Recommendations will also be offered for effective, ethical integration of AI in counseling supervision. Full article

Background/Objectives: Testicular cancer is one of the most common malignancies affecting young adult males. With the rise in artificial intelligence (AI) platforms, many patients seek health information online. Yet chatbot responses specific to testicular cancer remain unassessed. This study aims to evaluate the role of AI chatbots in providing patient information about testicular cancer in terms of its quality, readability and actionability. Methods: Fourteen frequently asked questions about testicular cancer were identified using Google Trends and the Cancer Council Australia website. Questions were then inputted into four different publicly accessible AI platforms: ChatSonic, Bing AI, ChatGPT 4.0 and Perplexity. Chatbot responses were recorded and evaluated using three validated instruments: DISCERN (1–5), Patient Education Materials Assessment Tool (PEMAT)-Understandability and Actionability (0–100%) and Flesch-Kincaid readability scores. Results: All platforms scored low on the DISCERN score with a median of 1 (interquartile range [IQR] 1–4). The median readability score was 34.1 (IQR 26.0–52.2), indicating a reading level suitable for college students. The median word count was 61.5 (IQR, 41.3–91.3). The overall PEMAT-Understandability was moderate (median 58.3, 50.0–66.7), whilst the PEMAT-Actionability was very poor (median 0, IQR 0–25). Conclusions: AI chatbots deliver moderately understandable information on testicular cancer, but this information is typically not actionable and is delivered at an above-average reading level. Despite this, patients may continue to use AI chatbots (AICs) to access health information. It is important that clinicians counsel patients on the benefits and downfalls of this strategy, advocating for the use of AICs as an adjunct rather than a replacement for clinician-led education. Full article

Digital mental health resources have expanded rapidly in the wake of the COVID-19 pandemic, offering new opportunities to improve access to mental healthcare through technologies such as AI chatbots, mobile apps, and online platforms. Despite this growth, significant challenges persist, including low user retention, limited digital literacy, unclear privacy regulations, and insufficient evidence of clinical effectiveness and safety. AI chatbots, which act as virtual therapists or companions, provide counseling and personalized support, but raise concerns about user dependence, emotional outcomes, privacy, ethical risks, and bias. User experiences are mixed: while some report enhanced social health and reduced loneliness, others question the safety, crisis response, and overall reliability of these tools, particularly in unregulated settings. Vulnerable and underserved populations may face heightened risks, highlighting the need for engagement with individuals with lived experience to define safe and supportive interactions. This review critically examines the empirical and grey literature on AI chatbot use in mental healthcare, evaluating their benefits and limitations in terms of access, user engagement, risk management, and clinical integration. Key findings indicate that AI chatbots can complement traditional care and bridge service gaps. However, current evidence is constrained by short-term studies and a lack of diverse, long-term outcome data. The review underscores the importance of transparent operations, ethical governance, and hybrid care models combining technological and human oversight. Recommendations include stakeholder-driven deployment approaches, rigorous evaluation standards, and ongoing real-world validation to ensure equitable, safe, and effective use of AI chatbots in mental healthcare. Full article

Artificial Intelligence (AI) has increasingly contributed to advancements in pharmaceutical practice, particularly by enhancing the pharmacist–patient relationship and improving medication adherence. This quantitative, descriptive, cross-sectional study investigated Eastern Romanian pharmacists’ perception of AI-based applications as effective optimization tools, correlating it with disruptive communication factors. An anonymous and online questionnaire was distributed to community pharmacists, examining sociodemographic characteristics, awareness of disruptive factors, and the perceived usefulness of AI. The sample included 437 respondents: pharmacists (55.6%), mostly female (83.8%), and aged between 25 and 44 (52.6%). Data analysis involved descriptive statistics and independent t-tests. The statistical analysis revealed a significantly positive perception (p < 0.001) of AI on pharmacist–patient communication. Respondents viewed AI as a valuable tool for reducing medication errors and optimizing counseling time, though they maintain a strong emphasis on genuine human interaction. Significant correlations were found between disruptive factors—such as noise and high patient volume—and the quality of communication. Participants also expressed an increased interest in applications like automatic prescription scheduling and the use of chatbots. The study concludes that a balanced implementation of AI technologies is necessary, one that runs parallel with the continuous development of pharmacists’ communication skills. Future research should focus on validating AI’s impact on clinical outcomes and establishing clear ethical guidelines regarding the use of patient data. Full article

Background/Objectives: With advancements in Large Language Models (LLMs), counseling chatbots are becoming essential tools for delivering scalable and accessible mental health support. Traditional evaluation scales, however, fail to adequately capture the sophisticated capabilities of these systems, such as personalized interactions, empathetic responses, and memory retention. This study aims to design a robust and comprehensive evaluation scale, the Comprehensive Evaluation Scale for LLM-Powered Counseling Chatbots (CES-LCC), using the eDelphi method to address this gap. Methods: A panel of 16 experts in psychology, artificial intelligence, human-computer interaction, and digital therapeutics participated in two iterative eDelphi rounds. The process focused on refining dimensions and items based on qualitative and quantitative feedback. Initial validation, conducted after assembling the final version of the scale, involved 49 participants using the CES-LCC to evaluate an LLM-powered chatbot delivering Self-Help Plus (SH+), an Acceptance and Commitment Therapy-based intervention for stress management. Results: The final version of the CES-LCC features 27 items grouped into nine dimensions: Understanding Requests, Providing Helpful Information, Clarity and Relevance of Responses, Language Quality, Trust, Emotional Support, Guidance and Direction, Memory, and Overall Satisfaction. Initial real-world validation revealed high internal consistency (Cronbach’s alpha = 0.94), although minor adjustments are required for specific dimensions, such as Clarity and Relevance of Responses. Conclusions: The CES-LCC fills a critical gap in the evaluation of LLM-powered counseling chatbots, offering a standardized tool for assessing their multifaceted capabilities. While preliminary results are promising, further research is needed to validate the scale across diverse populations and settings. Full article

The integration of generative AI (GenAI) in school-based mental health services presents new opportunities and challenges. This study focuses on the challenges of using GenAI chatbots as therapeutic tools by exploring secondary school students’ perceptions of such applications. The data were collected from students who had both theoretical and practical experience with GenAI. Based on Grodniewicz and Hohol’s framework highlighting the “Problem of a Confused Therapist”, “Problem of a Non-human Therapist”, and “Problem of a Narrowly Intelligent Therapist”, qualitative data from student reflections were examined using thematic analysis. The findings revealed that while students acknowledged AI’s benefits, such as accessibility and non-judgemental feedback, they expressed significant concerns about a lack of empathy, trust, and adaptability. The implications underscore the need for AI chatbot use to be complemented by in-person counselling, emphasising the importance of human oversight in AI-augmented mental health care. This study contributes to a deeper understanding of how advanced AI can be ethically and effectively incorporated into school mental health frameworks, balancing technological potential with essential human interaction. Full article

Background: Pharmacists need up-to-date knowledge and decision-making support in HIV care. We aim to develop MARVIN-Pharma, an adapted artificial intelligence-based chatbot initially for people with HIV, to assist pharmacists in considering evidence-based needs. Methods: From December 2022 to December 2023, an online needs-assessment survey evaluated Québec pharmacists’ knowledge, attitudes, involvement, and barriers relative to HIV care, alongside perceptions relevant to the usability of MARVIN-Pharma. Recruitment involved convenience and snowball sampling, targeting National HIV and Hepatitis Mentoring Program affiliates. Results: Forty-one pharmacists (28 community, 13 hospital-based) across 15 Québec municipalities participated. Participants perceived their HIV knowledge as moderate (M = 3.74/6). They held largely favorable attitudes towards providing HIV care (M = 4.02/6). They reported a “little” involvement in the delivery of HIV care services (M = 2.08/5), most often ART adherence counseling, refilling, and monitoring. The most common barriers reported to HIV care delivery were a lack of time, staff resources, clinical tools, and HIV information/training, with pharmacists at least somewhat agreeing that they experienced each (M ≥ 4.00/6). On average, MARVIN-Pharma’s acceptability and compatibility were in the ‘undecided’ range (M = 4.34, M = 4.13/7, respectively), while pharmacists agreed to their self-efficacy to use online health services (M = 5.6/7). Conclusion: MARVIN-Pharma might help address pharmacists’ knowledge gaps and barriers to HIV treatment and care, but pharmacist engagement in the chatbot’s development seems vital for its future uptake and usability. Full article

The IMPACT-FH study implemented strategies (packet, chatbot, direct contact) to promote family member cascade testing for familial hypercholesterolemia (FH). We evaluated the impact of genetic counseling (GC) on medical outcomes, strategy selection, and cascade testing. Probands (i.e., patients with FH) were recommended to complete GC and select sharing strategies. Comparisons were performed for both medical outcomes and strategy selection between probands with or without GC. GEE models for Poisson regression were used to examine the relationship between proband GC completion and first-degree relative (FDR) cascade testing. Overall, 46.3% (81/175) of probands completed GC. Probands with GC had a median LDL-C reduction of −13.0 mg/dL (−61.0, 4.0) versus −1.0 mg/dL (−16.0, 17.0) in probands without GC (p = 0.0054). Probands with and without GC selected sharing strategies for 65.3% and 40.3% of FDRs, respectively (p < 0.0001). Similarly, 27.1% of FDRs of probands with GC completed cascade testing, while 12.0% of FDRs of probands without GC completed testing (p = 0.0043). Direct contact was selected for 47 relatives in total and completed for 39, leading to the detection of 18 relatives with FH. Proband GC was associated with improved medical outcomes and increased FDR cascade testing. Direct contact effectively identified FH cases for the subset who participated. Full article

Background: Artificial intelligence (AI)-based chatbots have shown promise in providing counseling to patients with metabolic dysfunction-associated steatotic liver disease (MASLD). While ChatGPT3.5 has demonstrated the ability to comprehensively answer MASLD-related questions in English, its accuracy remains suboptimal. Whether language influences these results is unclear. This study aims to assess ChatGPT’s performance as a counseling tool for Italian MASLD patients. Methods: Thirteen Italian experts rated the accuracy, completeness and comprehensibility of ChatGPT3.5 in answering 15 MASLD-related questions in Italian using a six-point accuracy, three-point completeness and three-point comprehensibility Likert’s scale. Results: Mean scores for accuracy, completeness and comprehensibility were 4.57 ± 0.42, 2.14 ± 0.31 and 2.91 ± 0.07, respectively. The physical activity domain achieved the highest mean scores for accuracy and completeness, whereas the specialist referral domain achieved the lowest. Overall, Fleiss’s coefficient of concordance for accuracy, completeness and comprehensibility across all 15 questions was 0.016, 0.075 and −0.010, respectively. Age and academic role of the evaluators did not influence the scores. The results were not significantly different from our previous study focusing on English. Conclusion: Language does not appear to affect ChatGPT’s ability to provide comprehensible and complete counseling to MASLD patients, but accuracy remains suboptimal in certain domains. Full article

Artificial intelligence (AI) is rapidly transforming the field of medicine, announcing a new era of innovation and efficiency. Among AI programs designed for general use, ChatGPT holds a prominent position, using an innovative language model developed by OpenAI. Thanks to the use of deep learning techniques, ChatGPT stands out as an exceptionally viable tool, renowned for generating human-like responses to queries. Various medical specialties, including rheumatology, oncology, psychiatry, internal medicine, and ophthalmology, have been explored for ChatGPT integration, with pilot studies and trials revealing each field’s potential benefits and challenges. However, the field of genetics and genetic counseling, as well as that of rare disorders, represents an area suitable for exploration, with its complex datasets and the need for personalized patient care. In this review, we synthesize the wide range of potential applications for ChatGPT in the medical field, highlighting its benefits and limitations. We pay special attention to rare and genetic disorders, aiming to shed light on the future roles of AI-driven chatbots in healthcare. Our goal is to pave the way for a healthcare system that is more knowledgeable, efficient, and centered around patient needs. Full article

Glaucoma, the leading cause of irreversible blindness worldwide, comprises a group of progressive optic neuropathies requiring early detection and lifelong treatment to preserve vision. Artificial intelligence (AI) technologies are now demonstrating transformative potential across the spectrum of clinical glaucoma care. This review summarizes current capabilities, future outlooks, and practical translation considerations. For enhanced screening, algorithms analyzing retinal photographs and machine learning models synthesizing risk factors can identify high-risk patients needing diagnostic workup and close follow-up. To augment definitive diagnosis, deep learning techniques detect characteristic glaucomatous patterns by interpreting results from optical coherence tomography, visual field testing, fundus photography, and other ocular imaging. AI-powered platforms also enable continuous monitoring, with algorithms that analyze longitudinal data alerting physicians about rapid disease progression. By integrating predictive analytics with patient-specific parameters, AI can also guide precision medicine for individualized glaucoma treatment selections. Advances in robotic surgery and computer-based guidance demonstrate AI’s potential to improve surgical outcomes and surgical training. Beyond the clinic, AI chatbots and reminder systems could provide patient education and counseling to promote medication adherence. However, thoughtful approaches to clinical integration, usability, diversity, and ethical implications remain critical to successfully implementing these emerging technologies. This review highlights AI’s vast capabilities to transform glaucoma care while summarizing key achievements, future prospects, and practical considerations to progress from bench to bedside. Full article

Religion-based AI chatbots serve religious practitioners by bringing them godly wisdom through technology. These bots reply to spiritual and worldly questions by drawing insights or citing verses from the Quran, the Bible, the Bhagavad Gita, the Torah, or other holy books. They answer religious and theological queries by claiming to offer historical contexts and providing guidance and counseling to their users. A criticism of these bots is that they may give inaccurate answers and proliferate bias by propagating homogenized versions of the religions they represent. These “embodied spiritual machines” may likewise bear bias against women, their gender, and their societal roles. This paper crafts a concept intended to address this GPT issue by reimagining, modifying, and implementing a feminist approach to these chatbots. It examines the concepts and designs of these bots and how they address women-related questions. Along with the challenge of bringing gender and diversity-sensitive religious wisdom closer to the people through technology, the paper proposes a re-engineered model of a fair religion-based AI chatbot. Full article

Depression disorder is a disease that causes a deterioration of daily function and can induce thoughts of suicide. The Diagnostic and Statistical Manual of Mental Disorders Fifth Edition (DSM-5), which is the official reference of the American Psychiatry Association and is also used in Korea to identify depressive disorders, sets nine criteria for diagnosing depressive disorders. The lack of counseling personnel, including psychiatrists, and negative social perceptions of depressive disorders prevent counselors from being treated for depressive disorders. Natural language processing-based artificial intelligence (AI) services such as chatbots can help fill this need, but labeled datasets are needed to train AI services. In this study we collected data from AI Hub wellness consultations and crawls of the Reddit website to augment and build word dictionaries and analyze morphemes using the Kind Korean Morpheme Analyzer and Word2Vec. The collected datasets were labeled based on word dictionaries built according to nine DSM-5 depressive disorder diagnostic criteria. Full article