Multinational Attitudes Toward AI in Health Care and Diagnostics Among Hospital Patients

IMPORTANCE The successful implementation of artificial intelligence (AI) in health care depends on its acceptance by key stakeholders, particularly patients, who are the primary beneficiaries of AI-driven outcomes. OBJECTIVES To survey hospital patients to investigate their trust, concerns, and preferences toward the use of AI in health care and diagnostics and to assess the sociodemographic factors associated with patient attitudes. DESIGN, SETTING, AND PARTICIPANTS This cross-sectional study developed and implemented an anonymous quantitative survey between February 1 and November 1, 2023, using a nonprobability sample at 74 hospitals in 43 countries. Participants included hospital patients 18 years of age or older who agreed with voluntary participation in the survey presented in 1 of 26 languages. EXPOSURE Information sheets and paper surveys handed out by hospital staff and posted in conspicuous hospital locations. MAIN OUTCOMES AND MEASURES The primary outcome was participant responses to a 26-item instrument containing a general data section (8 items) and 3 dimensions (trust in AI, AI and diagnosis, preferences and concerns toward AI) with 6 items each. Subgroup analyses used cumulative link mixed and binary mixed-effects models. RESULTS In total, 13 806 patients participated, including 8951 (64.8%) in the Global North and 4855 (35.2%) in the Global South. Their median (IQR) age was 48 (34-62) years, and 6973 (50.5%) were male. The survey results indicated a predominantly favorable general view of AI in health care, with 57.6% of respondents (7775 of 13 502) expressing a positive attitude. However, attitudes exhibited notable variation based on demographic characteristics, health status, and technological literacy. Female respondents (3511 of 6318 [55.6%]) exhibited fewer positive attitudes toward AI use in medicine than male respondents (4057 of 6864 [59.1%]), and participants with poorer health status exhibited fewer positive attitudes toward AI use in medicine (eg, 58 of 199 [29.2%] with rather negative views) than patients with very good health (eg, 134 of 2538 [5.3%] with rather negative views). Conversely, higher levels of AI knowledge and frequent use of technology devices were associated with more positive attitudes. Notably, fewer than half of the participants expressed positive attitudes regarding all items pertaining to trust in AI. The lowest level of trust was observed for the accuracy of AI in providing information regarding treatment responses (5637 of 13 480 respondents [41.8%] trusted AI). Patients preferred explainable AI (8816 of 12 563 [70.2%]) and physician-led decision-making (9222 of 12 652 [72.9%]), even if it meant slightly compromised accuracy. CONCLUSIONS AND RELEVANCE In this cross-sectional study of patient attitudes toward AI use in health care across 6 continents, findings indicated that tailored AI implementation strategies should take patient demographics, health status, and preferences for explainable AI and physician oversight into account

Community Noise Exposure and its Effect on Blood Pressure and Renal Function in Patients with Hypertension and Cardiovascular Disease

Background: Road traffic noise (RTN) is a risk factor for cardiovascular disease (CVD) and hypertension; however, few studies have looked into its association with blood pressure (BP) and renal function in patients with prior CVD

Association between community noise and adiposity in patients with cardiovascular disease

Introduction: This study aimed to explore the effect of community noise on body mass index (BMI) and waist circumference (WC) in patients with cardiovascular disease (CVD). Materials and Methods: A representative sample of 132 patients from three tertiary hospitals in the city of Plovdiv, Bulgaria was collected. Anthropometric measurements were linked to global noise annoyance (GNA) based on different residential noise annoyances, day–evening–night (Lden), and nighttime (Lnight) road traffic noise exposure. Noise map Lden and Lnight were determined at the living room and bedroom façades, respectively, and further corrected to indoor exposure based on the window-opening frequency and soundproofing insulation. Results and Discussion: Results showed that BMI and WC increased (non-significantly) per 5 dB. The effect of indoor noise was stronger in comparison with that of outdoor noise. For indoor Lden, the effect was more pronounced in men, those with diabetes, family history of diabetes, high noise sensitivity, using solid fuel/gas for domestic heating/cooking, and living on the first floor. As regards indoor Lnight, its effect was more pronounced in those with low socioeconomic status, hearing loss, and using solid fuel/gas for domestic heating/cooking. GNA was associated with lower BMI and WC. Conclusion: Road traffic noise was associated with an increase in adiposity in some potentially vulnerable patients with CVD

Translocation of silica nanospheres through giant unilamellar vesicles (GUVs) induced by a high frequency electromagnetic field

Membrane model systems capable of mimicking live cell membranes were used for the first time in studying the effects arising from electromagnetic fields (EMFs) of 18 GHz where membrane permeability was observed following exposure.</jats:p

Translocation of silica nanospheres through giant unilamellar vesicles (GUVs) induced by a high frequency electromagnetic field

Membrane model systems capable of mimicking live cell membranes were used for the first time in studying the effects arising from electromagnetic fields (EMFs) of 18 GHz where membrane permeability was observed following exposure. A present lack of understanding of the mechanisms that drive such a rapid change in membrane permeabilization as well as any structural or dynamic changes imparted on biomolecules affected by high-frequency electromagnetic irradiation limits the use of 18 GHz EMFs in biomedical applications. A phospholipid, 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) labelled with a fluorescent marker 1,2-dioleoyl-sn-glycero-3-phosphoethanolamine-N-(lissamine rhodamine B sulfonyl) (rhodamine-DOPE) was used in constructing the giant unilamellar vesicles (GUVs). After three cycles of exposure, enhanced membrane permeability was observed by the internalisation of hydrophilic silica nanospheres of 23.5 nm and their clusters. All-atom molecular dynamics simulations of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) membranes exposed to high frequency electric fields of different field strengths showed that within the simulation timeframe only extremely high strength fields were able to cause an increase in the interfacial water dynamics characterized by water dipole realignments. However, a lower strength, high frequency EMF induced changes of the water hydrogen bond network, which may contribute to the mechanisms that facilitate membrane permeabilization in a longer timeframe

Abstracts Of The Proceedings And The Posters From The Third Scientific Session Of The Medical College Of Varna

October 2-3, 201

Multinational Attitudes Toward AI in Health Care and Diagnostics Among Hospital Patients

IMPORTANCE: The successful implementation of artificial intelligence (AI) in health care depends on its acceptance by key stakeholders, particularly patients, who are the primary beneficiaries of AI-driven outcomes.OBJECTIVES: To survey hospital patients to investigate their trust, concerns, and preferences toward the use of AI in health care and diagnostics and to assess the sociodemographic factors associated with patient attitudes.DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study developed and implemented an anonymous quantitative survey between February 1 and November 1, 2023, using a nonprobability sample at 74 hospitals in 43 countries. Participants included hospital patients 18 years of age or older who agreed with voluntary participation in the survey presented in 1 of 26 languages.EXPOSURE: Information sheets and paper surveys handed out by hospital staff and posted in conspicuous hospital locations.MAIN OUTCOMES AND MEASURES: The primary outcome was participant responses to a 26-item instrument containing a general data section (8 items) and 3 dimensions (trust in AI, AI and diagnosis, preferences and concerns toward AI) with 6 items each. Subgroup analyses used cumulative link mixed and binary mixed-effects models.RESULTS: In total, 13 806 patients participated, including 8951 (64.8%) in the Global North and 4855 (35.2%) in the Global South. Their median (IQR) age was 48 (34-62) years, and 6973 (50.5%) were male. The survey results indicated a predominantly favorable general view of AI in health care, with 57.6% of respondents (7775 of 13 502) expressing a positive attitude. However, attitudes exhibited notable variation based on demographic characteristics, health status, and technological literacy. Female respondents (3511 of 6318 [55.6%]) exhibited fewer positive attitudes toward AI use in medicine than male respondents (4057 of 6864 [59.1%]), and participants with poorer health status exhibited fewer positive attitudes toward AI use in medicine (eg, 58 of 199 [29.2%] with rather negative views) than patients with very good health (eg, 134 of 2538 [5.3%] with rather negative views). Conversely, higher levels of AI knowledge and frequent use of technology devices were associated with more positive attitudes. Notably, fewer than half of the participants expressed positive attitudes regarding all items pertaining to trust in AI. The lowest level of trust was observed for the accuracy of AI in providing information regarding treatment responses (5637 of 13 480 respondents [41.8%] trusted AI). Patients preferred explainable AI (8816 of 12 563 [70.2%]) and physician-led decision-making (9222 of 12 652 [72.9%]), even if it meant slightly compromised accuracy.CONCLUSIONS AND RELEVANCE: In this cross-sectional study of patient attitudes toward AI use in health care across 6 continents, findings indicated that tailored AI implementation strategies should take patient demographics, health status, and preferences for explainable AI and physician oversight into account

Global cross-sectional student survey on AI in medical, dental, and veterinary education and practice at 192 faculties

Background The successful integration of artificial intelligence (AI) in healthcare depends on the global perspectives of all stakeholders. This study aims to answer the research question: What are the attitudes of medical, dental, and veterinary students towards AI in education and practice, and what are the regional differences in these perceptions? Methods An anonymous online survey was developed based on a literature review and expert panel discussions. The survey assessed students' AI knowledge, attitudes towards AI in healthcare, current state of AI education, and preferences for AI teaching. It consisted of 16 multiple-choice items, eight demographic queries, and one free-field comment section. Medical, dental, and veterinary students from various countries were invited to participate via faculty newsletters and courses. The survey measured technological literacy, AI knowledge, current state of AI education, preferences for AI teaching, and attitudes towards AI in healthcare using Likert scales. Data were analyzed using descriptive statistics, Mann-Whitney U-test, Kruskal-Wallis test, and Dunn-Bonferroni post hoc test. Results The survey included 4313 medical, 205 dentistry, and 78 veterinary students from 192 faculties and 48 countries. Most participants were from Europe (51.1%), followed by North/South America (23.3%) and Asia (21.3%). Students reported positive attitudes towards AI in healthcare (median: 4, IQR: 3-4) and a desire for more AI teaching (median: 4, IQR: 4-5). However, they had limited AI knowledge (median: 2, IQR: 2-2), lack of AI courses (76.3%), and felt unprepared to use AI in their careers (median: 2, IQR: 1-3). Subgroup analyses revealed significant differences between the Global North and South (r = 0.025 to 0.185, all P &lt; .001) and across continents (r = 0.301 to 0.531, all P &lt; .001), with generally small effect sizes. Conclusions This large-scale international survey highlights medical, dental, and veterinary students' positive perceptions of AI in healthcare, their strong desire for AI education, and the current lack of AI teaching in medical curricula worldwide. The study identifies a need for integrating AI education into medical curricula, considering regional differences in perceptions and educational needs. Trial registrationNot applicable (no clinical trial)