Sztuczna inteligencja (artificial intelligence – AI) odgrywa coraz większą rolę w medycynie, wpływając na diagnostykę, leczenie oraz organizację systemów opieki zdrowotnej. W pracy przeanalizowano potencjalne korzyści oraz zagrożenia związane z wykorzystaniem AI w medycynie. W szczególności skupiono się na aspektach technicznych, etycznych i regulacyjnych, a także na wpływie AI na bezpieczeństwo pacjentów oraz skuteczność podejmowanych decyzji klinicznych. Przeprowadzono kompleksowy przegląd literatury w bazach danych PubMed, Scopus i Google Scholar, uwzględniając publikacje z lat 2019–2025. Na podstawie wyselekcjonowanych 52 artykułów dokonano jakościowej syntezy wyników, identyfikując kluczowe wyzwania oraz rekomendacje dotyczące dalszych badań i wdrażania AI w opiece zdrowotnej. Wyniki analizy wskazują, że AI znacząco zwiększa precyzję diagnostyczną i skuteczność terapii, jednocześnie niesie za sobą ryzyko błędów algorytmicznych, stronniczości modeli oraz naruszenia prywatności pacjentów. Skuteczne wdrożenie AI wymaga uregulowań prawnych, przejrzystych zasad stosowania oraz współpracy ekspertów z różnych dziedzin. Przyszłe badania powinny koncentrować się na opracowaniu mechanizmów zwiększających zaufanie do systemów AI i zapewniających ich odpowiedzialne wykorzystanie w medycynie.

Background: Reliable mesiodistal tooth-width measurements are essential for orthodontic diagnosis and treatment planning. Digital models obtained by scanning plaster casts may facilitate orthodontic workflows; however, the extent of agreement between manual caliper measurements and digital measurements on digitized plaster models remains clinically relevant, particularly at the individual-tooth level. Objective: To evaluate the agreement, repeatability, and inter-method differences between manual and digital mesiodistal tooth-width measurements from the canine to the first molar on digitized plaster models. Methods: Plaster models from 30 orthodontic patients were analyzed. The models were digitized using a 3Shape TRIOS 4 intraoral scanner. Mesiodistal widths of teeth 13–16, 23–26, 33–36, and 43–46 were measured using digital calipers on plaster models and using 3Shape Unite platform version 1.7.33.2 (3Shape A/S, Copenhagen, Denmark) on the corresponding digital models. Manual and digital measurements were each performed twice at 24 h intervals. For both methods, the mean of the two repeated measurements was used for inter-method comparison. Manual and digital intra-observer repeatability was assessed using the intraclass correlation coefficient (ICC). Inter-method differences (ΔMD = mean manual − digital) were analyzed using Bland–Altman analysis. Differences exceeding 0.3 mm were considered to exceed the predefined threshold of potential clinical relevance. Results: A total of 480 teeth were analyzed. Manual and digital intra-observer repeatability were excellent (ICC = 0.985 and ICC = 0.976, respectively). Manual measurements were on average 0.150 mm higher than digital measurements (SD = 0.407 mm; median = 0.145 mm; IQR = 0.29 mm), with 95% limits of agreement ranging from −0.647 mm to 0.947 mm. At the single-tooth level, 212 out of 480 measurements (44.2%) exceeded the predefined 0.3 mm threshold. First molars and selected premolars showed the highest discrepancy rates, whereas canines generally showed lower discrepancy rates. Conclusions: Manual and digital mesiodistal tooth-width measurements performed on digitized plaster models showed good agreement under the tested conditions. However, individual tooth-level differences exceeding the predefined 0.3 mm threshold were frequent, particularly for posterior teeth. Therefore, digital measurements obtained from digitized plaster models should be interpreted with caution in clinical situations requiring high precision at the level of individual tooth dimensions.

Background: Advances in dental technology have improved treatment quality but increased costs. In Poland, where the scope of services reimbursed by the National Health Fund (NFZ) is limited, the cost of dental treatment can be a financial barrier – especially for lower-income patients, particularly for older adults. Objective: The aim of this study was to analyze how income levels affect patients’ awareness of dental treatment costs and their decisions regarding available treatment options. Material and methods: A cross-sectional survey of 282 participants (dental patients, medical students, healthcare and education professionals, and their families) was conducted from late 2023 to early 2024. Data from a self administered questionnaire were analyzed in Python using chi-square tests, Cramér’s V, and phi coefficients (p < 0.05). Income was dichotomized (PLN 3,500) when necessary. Results: Lower-income patients (<PLN 3,500) were more likely to postpone check-ups due to cost (24.8% vs. 13.1%, p = 0.03) and preferred NHF-covered treatments, whereas higher-income individuals opted for private care. No significant associations were found regarding awareness of NHF benefits, installment usage, or treatment withdrawal rates. Conclusions: Income significantly affects dental care utilization, influencing check-up postponement and treatment choices. However, weak correlations suggest other factors – such as health awareness, service quality and appointment availability – also play key roles.