A novel explainable machine learning-based healthy ageing scale

Creating decision models for risk assessment of ski injuries is a challenging task. Ski injuries are rare events, but they carry a high cost, that is, can cause working or movement disabilities. Usually, ski risk assessment is performed on small‐scale, case‐controlled studies where the effect of a single factor is evaluated. Recently, data mining and machine learning algorithms are being employed for ski risk assessment and injury prediction. However, these models do not generally satisfy the need for interpretation of the decision model, do not provide explanations for the predictions, and in general do not ensure the completeness and consistency of decision rules. To make data mining and machine learning models useful, one needs to implement the aforementioned properties. Decision support systems are expected to have these properties; however, the process of building such decision support systems is still tedious: it has to consider human biases, assumptions, and subjective values, as well as focus on the decision problem being solved. We propose a method for extraction of decision models from data at hand. Our method DIDEX, Data Induced DEcision eXpert, builds models that have desirable properties for inclusion in decision support systems. The proposed method is used to build a decision model for ski injury prediction based on data from Mt. Kopaonik ski resort, Serbia. The results show that DIDEX generates up to a five times simpler model compared to the existing domain expert DEX models while having a 6% better predictive accuracy. Additionally, its predictive accuracy is comparable to similar machine learning algorithms, such as decision tree classifiers, random forest, and logistic regression.

Background: Heart diseases continue to be among the primary causes of morbidity and mortality worldwide, highlighting the need for novel diagnostic methods to accurately, early, and economically predict and diagnose heart diseases. Current physical explores upon expert knowledge, possibly missing intricate and non-linear associations between heart disease risk factors. This is where machine learning (ML)methods emerge as promising alternatives for prediction and diagnosis of heart diseases by emphasizing data availability. Methods: This paper attempts to find out how six popular machine learning algorithms namely, Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting (GB), K-Nearest Neighbors (KNN), and eXtreme Gradient Boosting (XGB) perform on a publicly available heart disease dataset collected from Kaggle. For better performance and to make them comparable to each other, basic preprocessing techniques have also been implemented. The performance of all these algorithms will be measured by their respective definitions of accuracy, precision, recall, and F1 score. Result: With an F1-score of 98.52%, Random Forest and XGBoost surpassed other models, yielding a superior accuracy in predictions, measuring 98.53%. The models recorded a perfect level of precision, 100%, coupled with high values of recall, exceeding 97%. Gradient Boosting recorded impressive results, receiving an accuracy score of 93.17%. Conclusion: The research findings revealed that the Random Forest and XGBoost models are very effective in the prediction and diagnosis of heart disease. These two models have the capacity to support a clinical decision. Novelty: That is to say, the novelty of this research is that it evaluates multiple machine learning models with comparison to each other. The role of machine learning in enhancing cardiovascular disease diagnosis as well as decision support systems in healthcare and educating and aware the people about heart care and health.

Development of Prediction Models for Healthy Ageing in Community-Dwelling Middle-Aged and Older Adults: A Longitudinal Study Using Machine Learning.

With most households in rapidly urbanizing cities in low- and medium-income countries using private and informal providers for basic healthcare, the need to establish linkages with the formal sector is paramount in the drive for universal health coverage. Successful and effective linkage of informal healthcare providers to the formal health system requires an understanding of prevailing contextual factors and how they can be modulated to support the linkages. Context plays a pivotal role in shaping the nature and success of any integration efforts. This paper, based on a qualitative study, explored the facilitating and constraining contextual influences shaping the linkage of informal healthcare providers into the formal health system in governance, service delivery, and data reporting. The research was conducted in Enugu and Anambra states in southeastern Nigeria. In-depth interviews were held with 12 senior healthcare managers, 16 primary healthcare facility managers, 32 informal providers, and 16 community leaders. Eight sex-disaggregated focus group discussions were held with health service users. Transcripts were coded in NVivo using a pre-defined coding framework comprising facilitators and constraints at the individual, organisational, and environmental levels. Individual factors that influence linkage of informal providers into the formal health system include personal attitudes towards linkage, capacity of informal providers to deliver quality services, nature of existing relationships between formal and informal providers, and trust in the formal health system. Organizational factors include leadership structure, coordination and accountability mechanisms, functional management capacity of the formal health system, and multiple regulatory frameworks. External factors include supportive health policies on integration, sustainable funding for continuous training and supportive supervision, and global agenda/support for integration. This study has provided valuable insights for decision makers and practitioners for harnessing the contextual factors to link informal healthcare providers successfully and effectively to the formal health system in order to improve access to quality health services in urban slums

Prayer camps and traditional healers have emerged recently as alternative sources of mental health care in Ghana. To increase their knowledge and collaboration between formal and informal mental health care providers, training and sensitization was organized for them. This study aimed at assessing beneficiaries' views about the impact of this intervention. We adopted narrative approach to qualitative enquiry using purposive sampling strategy to recruit formal and informal mental health care providers in Ghana for an in-depth interview. We analyzed the data thematically using QSR NVivo 12. Participants enhanced their knowledge about mental health and illness. They reported increased collaboration between formal and informal health care providers. Community psychiatric nurses (CPNs) give injections to patients instead of chaining and using shackles as was initially practiced. There are also regular visits by CPNs to traditional and spiritual healers to discuss the care of the mentally ill patients in their facilities. There has been an increased collaboration among healers of mental illness resulting in quick recovery of patients who seek care at traditional and spiritual healers. There is also abolition of chaining and using of shackles by these healers, with increasing respect for the human rights of patients.

BackgroundArtificial intelligence technology can be applied in several aspects of healthcare delivery and its integration into the Nigerian healthcare value chain is expected to bring about new opportunities. This study aimed at assessing the knowledge and perception of healthcare professionals in Nigeria regarding the application of artificial intelligence and machine learning in the health sector.MethodsA cross-sectional study was undertaken amongst healthcare professionals in Nigeria with the use of a questionnaire. Data were collected across the six geopolitical zones in the Country using a stratified multistage sampling method. Descriptive and inferential statistical analyses were undertaken for the data obtained.ResultsFemale participants (55.7%) were slightly higher in proportion compared to the male respondents (44.3%). Pharmacists accounted for 27.7% of the participants, and this was closely followed by medical doctors (24.5%) and nurses (19.3%). The majority of the respondents (57.2%) reported good knowledge regarding artificial intelligence and machine learning, about a third of the participants (32.2%) were of average knowledge, and 10.6% of the sample had poor knowledge. More than half of the respondents (57.8%) disagreed with the notion that the adoption of artificial intelligence in the Nigerian healthcare sector could result in job losses. Two-thirds of the participants (66.7%) were of the view that the integration of artificial intelligence in healthcare will augment human intelligence. Three-quarters (77%) of the respondents agreed that the use of machine learning in Nigerian healthcare could facilitate efficient service delivery.ConclusionThis study provides novel insights regarding healthcare professionals' knowledge and perception with respect to the application of artificial intelligence and machine learning in healthcare. The emergent findings from this study can guide government and policymakers in decision-making as regards deployment of artificial intelligence and machine learning for healthcare delivery.

Aim: The aim of the work is to evaluate the accuracy and precision in predicting heart disease using Support Vector Machine (SVM), Random forest (RF), Logistic Regression (LR), Decision Tree (DT) Classification algorithms. Materials and Methods: Classification algorithm is appealed on a heart dataset which consists of 180 records. A framework for heart disease prediction in the medical sector comparing Random forest, Logistic Regression, Decision Tree and SVM classifiers has been proposed and developed. The sample size was calculated as 55 in each group using G power 80%. Sample size was calculated using clincalc analysis, with alpha and beta values 0.05 and 0.5, 95% confidence, pretest power 80% and enrolment ratio 1. Results: The Novel Random Forest Algorithm (92.13%), Support Vector Machine (62.51%), Logistic Regression (84.89%), Decision Tree (86.25%) classifiers produce respectively. SVM, RF exists a statistically significant difference between the two groups (p=0.001,p=.004;p<0.05).LR, RF exists a statistically insignificant difference between the two groups (p=.103, P=.080;p>0.05) both with confidence interval 95%. Hence Random forest is better than SVM, RF, DT classifiers. Conclusion: The results show that the performance of RF is better when compared with SVM, LR and DT in terms of both precision and accuracy.

The application of Machine Learning (ML) in healthcare has opened unprecedented avenues for predictive analytics, diagnostics, and personalized medicine. However, the sensitivity of healthcare data and the ethical dilemmas associated with automated decision-making necessitate a rigorous ethical framework. This review paper aims to provide a comprehensive overview of the existing ethical frameworks that guide ML in healthcare and evaluates their adequacy in ad-dressing ethical challenges. Specifically, this article offers an in-depth examination of prevailing ethical constructs that oversee healthcare ML, spotlighting pivotal concerns: data protection, in-formed assent, equity, and patient autonomy. Various analytical approaches including quantitative metrics, statistical methods for bias detection, and qualitative thematic analyses are applied to address these challenges. Insights are further enriched through case studies of Clinical Decision Support Systems, Remote Patient Monitoring, and Telemedicine Applications. Each case is evaluated against existing ethical frameworks to identify limitations and gaps. Based on our com-prehensive review and evaluation, we propose actionable recommendations for evolving ethical guidelines. The paper concludes by summarizing key findings and underscoring the urgent need for robust ethical frameworks to guide ML applications in sensitive healthcare environments. Future work should focus on the development and empirical validation of new ethical frameworks that can adapt to emerging technologies and ethical dilemmas in healthcare ML.

Diabetes is a long-term health condition that impacts millions of people worldwide, and correct and timely diagnosis is required to manage it effectively. In the present research study, we propose an improved model of diabetes prediction based on the usage of Big Data Analytics (BDA) and Machine Learning (ML) algorithms for enhancing the predictability of diabetes. Our research is based on the ideas developed in the reference paper, where the application of BDA in enabling healthcare decisionmaking was proposed. With the inclusion of the usage of advanced ML algorithms like Decision Trees, Random Forest, Logistic Regression, Naive Bayes, and Artificial Neural Networks (ANN), we enhance predictability for the diagnosis of diabetes. We employed a comprehensive healthcare dataset with various health metrics, including blood pressure, cholesterol, BMI, smoking, and physical activity. The data was heavily preprocessed, ranging from feature engineering to normalization, to get better model performance. All the models were trained and tested on accuracy, precision, recall, and ROC-AUC. Among the models, Random Forest and ANN were better with higher accuracy, detecting diabetic patients at risk effectively. Our model not only increases prediction accuracy but also facilitates scalability with Big Data technology and practices. The ANN model, in particular, proved effective in finding complex patterns within the data, yielding a reliable solution towards real-time diabetes prediction. This effort illustrates the potential of integrating BDA and ML in healthcare for data-driven decision-making and supporting early diagnosis and management of diabetes. The model is deployed as an interactive user-friendly web application using Streamlet, where users can input health indicators and receive predictions from different models. Through its interactive interface, it facilitates healthcare professionals and patients by offering themreal-time information, thereby ultimately improving diabetes management and patient care.

Heart failure is a cause of death in India and early prediction of the patient outcomes can actually contribute to the clinical intervention. This paper has compared three machine learning algorithms namely the Logistic Regression, the random forest, and the Support Vector Machine to determine the extent to which each predictor was able to predict mortality based on clinical information of 299 heart failure patients. Logistic Regression was the most accurate (0.80) and the most precise (0.93) but it had a lower recall (0.56). At the same time, random forest scored the highest ROC-AUC at 0.837, whereas SVM had moderate scores on all boards. What we discovered is that Logistical Regression is a good starting point to clinical decision support system especially when the physicians require the answer to why a model is making some predictions. With this said, the trade-off between the precision and recalls that we saw in all three models indicates that there is still a problem in the machine learning in healthcare. False negatives may be quite serious in some cases hence this makes it particularly challenging to optimize these systems.

In the early time, very few studies were accomplished in Bengali functional sentences. However, the study on Bengali has incredibly increased for its structural diversity. Inspired by those studies, Functional sentence classification in Bengali language was completed including machine learning approaches to classify the sentences. Three types of Bengali functional sentences such as Assertive, Interrogative and Exclamatory have been considered for the research. So the leading purpose of the study is to classify the sentence and find out the best algorithm with comparing accuracy rate. Data have been collected, categorized and processed the dataset properly to avoid the conflict. Some popular machine learning algorithms such as Naive Bayes (NB), Decision Tree Classifier (DT), SVM, KNN, Random Forest (RF), and XGB Classifier have been implemented to compare accuracy rates. Parameters such as Precision, Recall, F1-Score, Support and Confusion matrix have been calculated for the comparison. The comparison demonstrated that performance of the Random Forest, SVC, and XGB Classifier is better than Naive Bayes and Decision Tree Classifier. Remarkable issue is that the Random Forest algorithm provided the highest performance value with an accuracy of 75.38% which is average performance for such a dataset.

Information and communication technology usage in the healthcare sector is not perceptible due to various challenges with increased healthcare needs. With the outburst of COVID-19, when the different countries announced lockdown and social distancing rules, it is crucial to predict a person's symptoms, which will help in the early diagnosis. In such situations, there is a tremendous growth seen in the usage of various technologies, such as remote health monitoring, Wireless Body Area Networks (WBANs), Machine Learning (ML), and Decision Support system (DSS). Hence, the chapter focuses on detecting diseases and associated symptoms using various ML algorithms. A total of 3073 patient data (heartbeat, snore, and body temperature) has been collected. The collected data were preprocessed to remove empty cells and zero values by replacing the mean of the cells. Later, the extracted features were used in Empirical Mode Decomposition (EWD) and Discrete Wavelet Transformation (DWT). Then, the optimized algorithms with the threshold values were identified by consulting doctors for accurate disease prediction. With the testing performance of various ML algorithms, such as Decision Tree Classifier (DTC), K-Nearest Neighbor (KNN), Gradient Descent (SGD), Naive Bayes (NB), Multilayer perceptron (MLP), Support Vector Machine (SVM), and Random Forest (RF), was compared. Performance evaluation parameters are accuracy, precision, F1 score, and recall. The results showed an average of 100% accuracy with SGD and SVM with DWT, whereas EMD, SVM, and MLP outperformed the state-of-the-art algorithms with 99.83% accuracy.

Photo by Josh Riemer on Unsplash
 Introduction
 With the rapid advancements in neurotechnological machinery and improved analytical insights from machine learning in neuroscience, the availability of big brain data has increased tremendously. Neurological health research is done using digitized brain data.[1] There must be adequate data governance to secure the privacy of subjects participating in brain research and treatments. If not properly regulated, the research methods could lead to significant breaches of the subject’s autonomy and privacy. This paper will address the necessity for neuroprotection laws, which effectively govern the use of big brain data to ensure respect for patient privacy and autonomy.
 Background
 Artificial intelligence and machine learning can be integrated with neuroscience big brain data to drive research studies. This integrative technology allows patterns of electrical activity in neurons to be studied in detail.[2]Specifically, it uses a robotic system which can reason, plan, and exhibit biologically intelligent behavior. Machine learning is a method of computer programming where the code can adapt its behavior based on big brain data.[3] The big brain data is the collection of large amounts of information for the purpose of deciphering patterns through computer analysis using machine learning.[4] The information that these technologies provide is extensive enough to allow a researcher to read a patient’s mind. AI and machine learning technologies work by finding the underlying structure of brain data, which is then described by patterns known as latent factors, eventually resulting in an understanding of the brain’s temporal dynamics.[5]
 Through these technologies, researchers are able to decipher how the human brain computes its performances and thoughts. However, due to the extensive and complex nature of the data processed through AI and machine learning, researchers may gain access to personal information a patient may not wish to reveal. From a bioethical lens, tensions arise in the realm of patient autonomy. Patients are not able to control the transmission of data from their brains that is analyzed by researchers. Governing brain data through laws may enhance the extent of patient privacy in the case where brain data is being used through AI technologies.[6] A responsible approach to governing brain data would require a sophisticated legal structure.
 Analysis
 Impact on Patient Autonomy and Privacy 
 In research pertaining to big brain data, the consent forms do not fully cover the vast amounts of information that is collected. According to research, personal data has become the most sought out commodity to provide content to corporations and the web-based service industry. Unfortunately, data leaks that release private information frequently occur.[7] The storage of an individual’s data on technologies accessible on the internet during research studies makes it vulnerable to leaks, jeopardizing an individual’s privacy. These data leaks may cause the patient to be identified easily, as the degree of information provided by AI technologies are personalized and may be decoded through brain fingerprinting methods.[8]
 There has been an extensive growth in the development and use of AI. It is efficient in providing information to radiologists who diagnose various diseases including brain cancer and psychiatric disease, and AI assists in the delivery of telemedicine.[9] However, the ethical pitfall of reduced patient autonomy must be addressed by analyzing current AI technologies and creating more options for patient preference in how the data may be used. For instance, facial recognition technology[10] commonly used in health care produces more information than listed in common consent forms, threatening to undermine informed consent. Facial recognition software collects extensive data and may disclose more information than a person would prefer to provide despite being a useful tool for diagnosing medical and genetic conditions.[11] In addition, people may not be aware that their images are being used to generate more clinical data for other purposes. It is difficult to guarantee the data is anonymized. Consent requirements must include informing people about the complexity of the potential uses of the data; software developers should maximize patient privacy.[12] Furthermore, there is a “human element” in the use of AI technologies as medical providers control the use and the extent to which data is captured or accessed through the AI technologies.[13] People must understand the scope of the technology and have clear communication with the physician or health care provider about how the medical information will be used. 
 Existing Laws for Brain Data Governance 
 A strict system of defined legal responsibilities of medical providers will ensure a higher degree of patient privacy and autonomy when AI technologies and data from machine learning are used. Governing specific algorithmic data is crucial in safeguarding a patient’s privacy and developing a gold standard treatment protocol following the procurement of the information.[14] Certain AI technologies provide more data than others, and legal boundaries should be established to ensure strong performance, quality control, and scope for patient privacy and autonomy. For instance, currently AI technologies are being used in the realm of intensive neurological care. However, there is a significant level of patient uncertainty about how much control patients have over the data’s uses.[15] Calibrated legal and ethical standards will allow important brain data to be securely governed and monitored.
 Once brain signals are recorded and processed from one individual, the data may be merged with other data in Brain Computer Interface Technology (BCI).[16] To ensure a right and ability to retrieve personal data or pull it from the collection, specific regulations for varying types of data are needed.[17] The importance of consent and patient privacy must be considered through giving patients a transparent view of how brain data is governed.[18] The legal system must address discriminatory issues and risks to patients whose data is used in studies. Laws like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Protection Act (CCPA) can serve as effective models to protect aggregated data. These laws govern consumer information and ensure the compliance when personal data is collected.[19] California voters recently approved expansion of the CCPA to health data. The Washington Privacy Act, which would have provided rights to access, change, and withdraw personal data, failed to pass. Other states should improve privacy as well,[20] although a federal bill would be preferable. Scientists at the Heidelberg Academy of Sciences argue for data security to be governed in a manner that balances patient privacy and autonomy with the commercial interests of researchers.[21] The balance could be achieved through privacy protections like those in the Washington Privacy Act. Although the Health Insurance Portability and Accountability Act (HIPAA) provides an overall framework to deter the likelihood of dangers to patient protection and privacy, more thorough laws are warranted to combat pervasive data transfer and analysis that technology has brought to the health care industry.[22] Breaches of patient privacy under current HIPAA regulations include releasing patient information to a reporter without their consent and sending HIV data to a patient’s employer without consent.[23] HIPAA does not cover information being shared with outside contractors who do not have an agreement with technology companies to keep patient data confidential. HIPAA regulations also do not always address blatant breaches on patient data confidentiality.[24] Patients must be provided with methods to monitor the data being analyzed to be able to view the extent of private information being generated via AI technologies. In health research, the medical purposes of better diagnosis, earlier detection of diseases, or prevention are ethical justifications for the use of the data if it was collected with permission, the person understood and approved the uses of the data, and the data was deidentified.
 A standard governance framework is required in providing the fairest system of care to patients who allow their brain data to be examined. Informed consent in the neuroscience field could reaffirm the privacy and autonomy of patients by ensuring that they understand the type of information collected. Laws also could protect data after a patient’s death. Malpractice in the scope of brain data could give people a cause of action critical in safeguarding patient’s rights. Data breach lawsuits will become common but generally do not cover deidentified data that becomes part of big data collection. A more synchronized approach to the collection and consent process will encourage an understanding of how big data is used to diagnose and treat patients. Some altruistic people may even be more likely to consent if they know the largescale data collection is helpful to treat and diagnose people. Others should have the ability to opt out of sharing neurological data, especially when there is not certainty surrounding deidentification.[25]
 Conclusion
 Artificial intelligence and machine learning technologies have the potential to aid in the diagnosis and treatment of people globally by extracting and aggregating brain data specific to individuals. However, the secure use of the data is necessary to build trust between care providers and patients, as well as in balancing the bioethical principles of beneficence and patient autonomy. We must ensure the highest quality of care to patients, while protecting their privacy, informed consent, and clinical trust. More sophis

Reflex cutaneous vasodilation during heating is attenuated in healthy human aging secondary to blunted increases in efferent skin sympathetic nervous system activity (SSNA) and reductions in end-organ sensitivity. Whether age-related alterations in the mean body temperature ( b) threshold for increasing SSNA and/or the sensitivity of responses are evident with aging have not been examined. We tested the hypotheses that the Tb threshold for SSNA and cutaneous vascular conductance (CVC) would be increased, but the sensitivity would be reduced, with aging. Reflex vasodilation was induced in 13 young (23 ± 3 y) and 13 older (67 ± 7 y) adults using a water-perfused suit to systematically increase mean skin and esophageal temperatures. SSNA (peroneal microneurography) and red cell flux (laser Doppler flowmetry) in the innervated dermatome were continuously measured. SSNA was normalized to baseline; CVC was normalized as a percentage of maximal CVC. Baseline b was lower in older adults (36.0 ± 0.4°C vs 36.4 ± 0.3°C; p = 0.005). During passive heating, the ∆ b thresholds for increasing SSNA and CVC were greater (1.3 ± 0.4°C vs 0.9 ± 0.3°C; p = 0.007 and 1.3 ± 0.4°C vs 0.8 ± 0.3°C; p = 0.002, respectively) in older adults. The slope of the relation between both SSNA (0.31 ± 0.23 vs 0.13 ± 0.10 V⋅s⋅°C −1; p = 0.01) and CVC (87.5 ± 50.1 vs 32.4 ± 18.1%max⋅°C−1; p = 0.002) vs b was lower in older adults. The relative b threshold for activation of SSNA and the initiation of reflex cutaneous vasodilation is higher in older adults, and once activated, the sensitivity of both responses is diminished, supporting the concept that the efferent component of the thermoregulatory reflex arc is impaired in healthy aging. Abbreviations: CI: confidence interval; CVC: cutaneous vascular conductance; SSNA: skin sympathetic nervous system activity; b: mean body temperature; Tes: esophageal temperature; sk: mean skin temperature.

BackgroundThe integration of artificial intelligence (AI) holds substantial potential to alter diagnostics and treatment in health care settings. However, public attitudes toward AI, including trust and risk perception, are key to its ethical and effective adoption. Despite growing interest, empirical research on the factors shaping public support for AI in health care (particularly in large-scale, representative contexts) remains limited.ObjectiveThis study aimed to investigate public attitudes toward AI in patient health care, focusing on how AI attributes (autonomy, costs, reliability, and transparency) shape perceptions of support, risk, and personalized care. In addition, it examines the moderating role of sociodemographic characteristics (gender, age, educational level, migration background, and subjective health status) in these evaluations. Our study offers novel insights into the relative importance of AI system characteristics for public attitudes and acceptance.MethodsWe conducted a factorial vignette experiment with a probability-based survey of 3030 participants from Germany’s general population. Respondents were presented with hypothetical scenarios involving AI applications in diagnosis and treatment in a hospital setting. Linear regression models assessed the relative influence of AI attributes on the dependent variables (support, risk perception, and personalized care), with additional subgroup analyses to explore heterogeneity by sociodemographic characteristics.ResultsMean values between 4.2 and 4.4 on a 1-7 scale indicate a generally neutral to slightly negative stance toward AI integration in terms of general support, risk perception, and personalized care expectations, with responses spanning the full scale from strong support to strong opposition. Among the 4 dimensions, reliability emerges as the most influential factor (percentage of explained variance [EV] of up to 10.5%). Respondents expect AI to not only prevent errors but also exceed current reliability standards while strongly disapproving of nontraceable systems (transparency is another important factor, percentage of EV of up to 4%). Costs and autonomy play a comparatively minor role (percentage of EVs of up to 1.5% and 1.3%), with preferences favoring collaborative AI systems over autonomous ones, and higher costs generally leading to rejection. Heterogeneity analysis reveals limited sociodemographic differences, with education and migration background influencing attitudes toward transparency and autonomy, and gender differences primarily affecting cost-related perceptions. Overall, attitudes do not substantially differ between AI applications in diagnosis versus treatment.ConclusionsOur study fills a critical research gap by identifying the key factors that shape public trust and acceptance of AI in health care, particularly reliability, transparency, and patient-centered approaches. Our findings provide evidence-based recommendations for policy makers, health care providers, and AI developers to enhance trust and accountability, key concerns often overlooked in system development and real-world applications. The study highlights the need for targeted policy and educational initiatives to support the responsible integration of AI in patient care.