Background/Objectives: Digital health interventions are increasingly used to support person-centered care (PCC) in chronic disease management, yet it remains unclear which PCC components are most consistently enabled by digital tools and how these relate to outcomes. This study synthesized evidence on digitally supported PCC for adults with chronic conditions, examining how interventions operationalize PCC and which clinical, patient-reported, and implementation outcomes are reported. Methods: A structured literature synthesis was conducted according to PRISMA guidelines across a heterogeneous evidence base, including randomized and pragmatic trials, observational studies, qualitative studies, and systematic reviews. The review protocol was pre-registered in the Open Science Framework (OSF) Registries. Results: Across 16 included studies, digital solutions most consistently supported PCC through enhanced situational awareness via self-monitoring, strengthened partnership through two-way communication and coaching, and reinforced shared documentation through co-created health plans. Benefits were reported most consistently for process and experience outcomes, such as perceived access to support, engagement, and empowerment. Evidence for sustained long-term clinical improvements, such as glycemic control, was mixed and frequently limited by short follow-up periods and variation in intervention integration. Conclusions: Digitalization can strengthen PCC when embedded within relational care models and organizational workflows that translate patient-generated data into meaningful action. Future work should utilize clearer PCC operationalization, longer follow-up, and routine reporting of equity outcomes, alongside targeted training for healthcare professionals delivering PCC in digital encounters.

Uvod: Dostopnost kakovostne zdravstvene oskrbe na področju duševnega zdravja je lahko otežena zaradi različnih omejitev, kot so geografske ovire, pomanjkanje strokovnjakov in stigma. Namen pregleda količine literature je analizirati prednosti in slabosti uporabe telemedicine in telepsihiatrije v obravnavi pacientov z duševnimi motnjami pri psihiatričnih posvetih in terapijah.Metode: Izveden je bil pregled obsega literature, ki je vključeval analizo in sintezo posameznih raziskav. Iskanje po podatkovnih bazah je bilo izvedeno z uporabo Boolovih operaterjev in ustreznih ključnih besed v angleškem jeziku (»telepsychiatry«, »benefits«, »disadvantages«, »psychotherapy«) v bazah PubMed, CINAHL, PsycArticles, ScienceDirect in Google Scholar. Upoštevane so bile raziskave, objavljene v zadnjem desetletju.Rezultati: Ugotovitve so bile razvrščene v eno glavno temo in pet podtem: tehnološki izzivi, zunanje motnje, ovire pri vzpostavljanju terapevtskega odnosa, izboljšana dostopnost in višja kakovost oskrbe. Ugotovljeno je bilo, da telemedicina in telepsihiatrija povečujeta dostopnost in priročnost za paciente, zlasti na podeželju, vendar poraja pomisleke o poglobljenosti terapevtskega odnosa in varovanja zasebnosti.Diskusija in zaključek: Telemedicina in telepsihiatrija predstavljata obetavno pot za izboljšanje dostopa do storitev na področju duševnega zdravja ter za spodbujanje oskrbe, usmerjene k pacientu. Vendar ostajajo številne pomembne ovire, ki zahtevajo dodatne empirične raziskave in prilagoditve na strukturni ravni, da bi ju uspešno vključili v vsakodnevno klinično prakso.

Chronological age dominates cardiovascular (CVD) risk prediction, but glycan-based measures may better reflect biological ageing. In this secondary analysis of a six-month randomized controlled trial in Slovenia, we examined whether IgG glycosylation–derived biological age (GlycanAge) tracked short-term changes in estimated CVD risk. Adults without diagnosed CVD but with at least one risk factor (119 enrolled; 101 completed the final assessment) received digital risk visualization, continuous glucose monitoring, their combination, or usual care. SCORE2 showed small numerical changes in some intervention groups, but GlycanAge remained largely stable, with only modest and inconsistent change across groups, and no association between change in SCORE2 and change in the GlycanAge–chronological age gap was detectable. Over this short timeframe, GlycanAge did not behave as a surrogate of conventional risk modification; whether it instead reflects slower immune-inflammatory ageing processes complementary to established CVD risk algorithms will require longer studies to establish.

BACKGROUND Large language models (LLMs) are increasingly used and evaluated in health professions education, including studies assessing model performance on healthcare examination questions. The rapid growth and heterogeneity of this literature make it difficult to track research concentration, collaboration patterns, and emerging themes. OBJECTIVE To map publication trends, key contributors, collaboration networks, and thematic hotspots in research on LLM-supported exam solving in healthcare education. METHODS We conducted a bibliometric analysis of publications from 2023–2025. Searches were performed in PubMed, Scopus, CINAHL Ultimate (EBSCOhost), and Web of Science using structured terms for AI/LLMs (eg, ChatGPT, generative AI, large language models) combined with healthcare education and training concepts. Eligible studies addressed AI-based technologies within healthcare education or training contexts; studies focused solely on clinical practice or non-educational applications were excluded. Bibliographic metadata from PubMed (TXT) and Scopus (BIB) were merged and analyzed using bibliometrix/Biblioshiny (R) and VOSviewer to quantify productivity, collaboration (including international co-authorship), and keyword co-occurrence patterns. RESULTS The dataset comprised 262 documents from 158 sources, with an annual publication growth rate of 36.58% and a mean document age of 1.83 years. A total of 1,351 authors contributed (mean 5.97 co-authors per document); international co-authored publications accounted for 13.36%. Most records were journal articles (253/262), followed by letters (8/262) and one conference paper. Annual output rose from 52 (2023) to 113 (2024; +117.3%), then decreased to 97 (2025; −14.2% vs 2024) while remaining above 2023 levels. JMIR Medical Education published the most articles on this topic (34/262), followed by Scientific Reports (9/262) and BMC Medical Education (7/262). Frequent keywords included “humans” (n=144), “artificial intelligence” (n=82), “generative AI” (n=30), and “large language models” (n=20); education-focused terms such as “educational measurement/methods” were also prominent (n=76). CONCLUSIONS Research on LLMs and exam performance in healthcare education expanded rapidly from 2023–2025, with publication activity concentrated in a limited set of journals and relatively low international collaboration. Thematic patterns emphasize assessment-related outcomes and LLM/ChatGPT performance, supporting the need for more comparable, transparent reporting (eg, prompts and model versions) and education-centered outcomes beyond accuracy in future studies. CLINICALTRIAL /

Animal-assisted interventions can support emotional well-being and social engagement, but their use may be limited by allergies, infection risks, and animal-handling requirements. As a scalable alternative, artificial intelligence-based pet therapy, including socially assistive and robotic pets, has been introduced in health care and community settings. This systematic review, conducted in accordance with PRISMA guidelines, searched PubMed, CINAHL Ultimate/MEDLINE, Scopus, Web of Science, and SAGE for studies published between 2014 and 2025 that evaluated the reported effects of AI-based pet therapy on patient outcomes. Of the 584 records identified, 27 studies met the inclusion criteria after duplicate removal and screening. Most studies involved older adults with dementia, although children, veterans, and community-dwelling adults were also represented. Across studies, AI-based pet therapy was associated with reduced agitation, anxiety, stress, and pain, as well as improved mood, communication, and social engagement. PARO was the most frequently studied robotic pet, and interventions were typically delivered 1–3 times weekly for 30–60 min over 4–12 weeks. Overall, AI-based pet therapy appears to be a promising complementary non-pharmacological approach, particularly for people with dementia and hospitalized children, although stronger evidence from larger, more standardized, and longer-term studies is still needed.

Uvod: Zaposleni v zdravstveni negi se pri svojem delu soočajo s številnimi duševnimi, čustvenimi in fizičnimi obremenitvami, kar lahko vpliva na njihovo zadovoljstvo na delovnem mestu. Namen raziskave je preučiti učinkovitost klinične supervizije na zadovoljstvo na delovnem mestu pri zaposlenih v zdravstveni negi.Metode: Uporabljen je bil pregled literature v mednarodnih podatkovnih bazah v novembru 2024. Vključitveni kriteriji so bile raziskave, ki temeljijo na kvantitativni in kvalitativni metodologiji, ter sistematični pregledi literature v angleškem jeziku, ki se nanašajo na raziskovano temo in so bili objavljeni v zadnjih desetih letih. Raziskave so bile s pomočjo hierarhije dokazov razvrščene po nivojih in predstavljene v preglednici.Rezultati: Izmed 106 zadetkov so bili v končno analizo vključeni štirje izvirni znanstveni članki in trije pregledni znanstveni članki. V tretji nivo hierarhije dokazov je bil uvrščen en članek, v četrti nivo en članek, v peti nivo en članek, v šesti nivo en članek in v sedmi nivo trije članki. Kljub temu, da klinična supervizija v zdravstveni negi ni pogosto uporabljena, so bili na podlagi pregleda literature ugotovljeni pozitivni učinki. Pregled literature je pokazal tudi metodološke pomanjkljivosti, kot so raznolikost metodologij, odsotnost kontrolne skupine in majhen vzorec.Diskusija in zaključek: Na podlagi analize je bilo ugotovljeno, da klinična supervizija pozitivno vpliva na zaposlene v zdravstveni negi ter prispeva k ohranjanju in izboljšanju zadovoljstva z delom. V prihodnje bi se bilo smiselno osrediniti na dolgoročne učinke klinične supervizije