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Can platform literacy protect vulnerable young people against the risky affordances of social media platforms?

A qualitative study of young people with mental health difficulties sought to understand their digital experiences and identify whether their digital literacy helps them cope with online problems. The findings reveal how young people’s encounters with extreme online risk are amplified by platforms’ promotion of trending and viral content and intensified through the personalisation of content that can ‘trigger’ individual vulnerabilities. We conceptualise these twin processes in terms of risky affordances and sho…

Information, Communication & Society · Health

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Optimizing Treatment Strategies in the Bipolar Disorder Spectrum With Classical AI Approaches: Systematic Review of Performance, Bias, and Clinical Applicability

Bipolar disorder (BD) is a complex and heterogeneous psychiatric condition, characterized by fluctuating clinical courses that affect approximately 1%-2% of the global population in their lifetime. Despite pharmacological advances, treatment response varies significantly among patients, making the identification of individualized treatment strategies a major challenge. Artificial Intelligence (AI), through its classical approaches, has emerged as a powerful tool in precision psychiatry to identify subtle pattern…

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A Supervised Fine-Tuned Large Language Model for Lifestyle Management in Patients With Prostate Cancer: Development and Evaluation Study

Lifestyle interventions for patients with prostate cancer have been shown to improve treatment adherence and quality of life. However, there remains a lack of large language models (LLMs) capable of delivering individualized and professional lifestyle recommendations under clearly defined medical safety boundaries and controlled evidence sources. This study aimed to develop and evaluate a supervised fine-tuned LLM-PCaPLMM_SFT (Prostate Cancer Patient Lifestyle Management Model via Supervised Fine-Tuning)-to supp…

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Multiturn Large Language Model-Based Conversational Agents for Patients With Cancer and Caregivers: Scoping Review

Large language model (LLM)-based conversational agents are increasingly used in health care, yet their capacity to support genuine multiturn dialogue remains underexplored. In oncology, where patients and caregivers experience complex informational and emotional needs throughout the disease trajectory, conversational agents may support information provision, symptom consultation, and emotional assistance. However, research specifically examining multiturn conversational agents designed for patients with cancer a…

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Enhancing objective structured clinical examination performance through an artificial intelligence virtual patient: a proof-of-concept study

The application of artificial intelligence (AI) in simulating detailed patient-doctor interactions for objective structured clinical examinations (OSCEs) remains emerging. This study aimed to evaluate an AI virtual patient (AIVP) innovation designed to support medical education through interactive patient simulations and feedback. This prospective mixed-methods pilot recruited final-year medical students during their critical care term. Two cohorts were examined: a volunteer AIVP group (n = 43) and an educationa…

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Can AI assist in reducing diagnostic error? A narrative review

Diagnostic error, defined as missed, wrong, or delayed diagnoses or those not communicated to patients, is common, affecting 5-10 % of hospital admissions and clinic visits. Such errors cause patient harm in up to 1 in 100 of such encounters and account for 10 % of all hospital deaths and serious adverse events. About 80 % of diagnostic errors are potentially preventable, most resulting from flaws in clinician reasoning in formulating and testing diagnostic hypotheses. The advent of artificial intelligence (AI),…

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Multimodal AI in Biomedicine: Pioneering the Future of Biomaterials, Diagnostics, and Personalized Healthcare

Multimodal artificial intelligence (AI) is driving a paradigm shift in modern biomedicine by seamlessly integrating heterogeneous data sources such as medical imaging, genomic information, and electronic health records. This review explores the transformative impact of multimodal AI across three pivotal areas: biomaterials science, medical diagnostics, and personalized medicine. In the realm of biomaterials, AI facilitates the design of patient-specific solutions tailored for tissue engineering, drug delivery, a…

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Current AI technologies in cancer diagnostics and treatment

Cancer continues to be a significant international health issue, which demands the invention of new methods for early detection, precise diagnoses, and personalized treatments. Artificial intelligence (AI) has rapidly become a groundbreaking component in the modern era of oncology, offering sophisticated tools across the range of cancer care. In this review, we performed a systematic survey of the current status of AI technologies used for cancer diagnoses and therapeutic approaches. We discuss AI-facilitated im…

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Current AI technologies in cancer diagnostics and treatment

Large Language Models in Healthcare and Medical Applications: A Review

This paper provides a systematic and in-depth examination of large language models (LLMs) in the healthcare domain, addressing their significant potential to transform medical practice through advanced natural language processing capabilities. Current implementations demonstrate LLMs' promising applications across clinical decision support, medical education, diagnostics, and patient care, while highlighting critical challenges in privacy, ethical deployment, and factual accuracy that require resolution for resp…

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A Technological Review of Digital Twins and Artificial Intelligence for Personalized and Predictive Healthcare

Digital transformation is reshaping the healthcare field by streamlining diagnostic workflows and improving disease management. Within this transformation, Digital Twins (DTs), which are virtual representations of physical systems continuously updated by real-world data, stand out for their ability to capture the complexity of human physiology and behavior. When coupled with Artificial Intelligence (AI), DTs enable data-driven experimentation, precise diagnostic support, and predictive modeling without posing di…

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AI-induced Deskilling in Medicine: A Mixed-Method Review and Research Agenda for Healthcare and Beyond

Abstract The integration of Artificial Intelligence (AI) in healthcare is reshaping clinical practice, offering both opportunities for enhanced decision-making and risks of skill degradation among medical professionals. This growing impact calls for a comprehensive evaluation of its effects on medical expertise. This study presents a mixed-method literature review, combining systematic analysis with narrative synthesis to examine AI-induced deskilling and upskilling inhibition-the erosion of medical expertise an…

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Personalized Nutrition in the Era of Digital Health: A New Frontier for Managing Diabetes and Obesity

The integration of digital health technologies with personalized nutrition offers a transformative approach for managing diabetes and obesity. This emerging paradigm extends beyond generic dietary recommendations by tailoring interventions based on genetic, epigenetic, microbiome, and real-time metabolic data. Tools such as continuous glucose monitors (CGMs), artificial intelligence (AI)-driven meal planning, and mobile health applications enable dynamic dietary adjustments and improved disease monitoring. Data…

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Artificial Intelligence in Clinical Medicine: Challenges Across Diagnostic Imaging, Clinical Decision Support, Surgery, Pathology, and Drug Discovery

Aims/Background: The growing integration of artificial intelligence (AI) into clinical medicine has opened new possibilities for enhancing diagnostic accuracy, therapeutic decision-making, and biomedical innovation across several domains. This review is aimed to evaluate the clinical applications of AI across five key domains of medicine: diagnostic imaging, clinical decision support systems (CDSS), surgery, pathology, and drug discovery, highlighting achievements, limitations, and future directions. Methods: A…

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Artificial Intelligence in Clinical Medicine: Challenges Across Diagnostic Imaging, Clinical Decision Support, Surgery, Pathology, and Drug Discovery

Artificial intelligence in healthcare and medicine: clinical applications, therapeutic advances, and future perspectives

Healthcare systems worldwide face growing challenges, including rising costs, workforce shortages, and disparities in access and quality, particularly in low- and middle-income countries. Artificial intelligence (AI) has emerged as a transformative tool capable of addressing these issues by enhancing diagnostics, treatment planning, patient monitoring, and healthcare efficiency. AI's role in modern medicine spans disease detection, personalized care, drug discovery, predictive analytics, telemedicine, and wearab…

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Artificial intelligence in nursing practice: a qualitative study of nurses’ perspectives on opportunities, challenges, and ethical implications

BACKGROUND: The study aims to explore nurses' views on the effects of artificial intelligence (AI) in nursing, focusing on their understanding, practical applications, ethical considerations, and perceived opportunities and threats. METHODS: This qualitative study used semi[Formula: see text]structured interviews to gain comprehensive insights from clinical nurses, adhering to the Standards for Reporting Qualitative Research for methodological rigor. After obtaining ethical approval, researchers conducted semi[F…

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