Brain Stroke Detection System Based on CT Image Using Deep Learning

Artificial intelligence (AI) in health-care is an emerging trend identified as a collection of technologies, programmed to sense, comprehend, act, and learn. Its performance is appreciated for the administrative and clinical healthcare functions and also for research and training purposes. AI includes natural brainpower, instrument learning, deep learning, neural networks, robotics, multiagent organisms, modernization in healthcare, and indistinct reasoning. Health includes healthcare, diagnostics, sanatoriums, and telemedicine, medications, therapeutic utensils and supplies, health assurance, and medical data. Healthcare curriculums are restricted by the unavailability of clinicians and inadequate dimensions, and AI amends these insufficiencies. The importance of AI in India has become a key technology for improving the effectiveness, value, cost, and reach of healthcare. The ethical issues for applying AI in healthcare must be analyzed and standardized. The encounters to the use of AI in health-care were recognized primarily through an analysis of literature, interviews, and roundtable inputs.

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

The integration of Artificial Intelligence (AI) in healthcare holds significant promise, yet remains constrained by data scarcity, privacy and ethical concerns, and the inherent complexity of medical tasks. This review explores the intersection of deep generative modeling and federated learning, two complementary paradigms that enable collaborative and privacy-preserving innovation. We survey recent trends in deep generative models applied to healthcare, with a particular focus on federated settings. The review is structured around model architectures, providing a taxonomy-driven analysis that emphasizes solutions tailored to the heterogeneity of healthcare data. Although this review initially intended to cover multimodal applications, the current literature is largely limited to single-modality models. Among deep generative approaches, Generative Adversarial Networks dominate the field, particularly in domains such as medical image synthesis and data augmentation. In contrast, alternative architectures, including variational autoencoders, diffusion models, and autoregressive models, remain barely explored in federated healthcare scenarios. Notably, the number of related publications has increased substantially in recent years, rising from 1 in 2020 to 16 in 2024, reflecting the growing interest and momentum in this area. This paper highlights key challenges, open research questions, and future directions for the development of trustworthy, distributed generative AI in healthcare.

The integration of Artificial Intelligence (AI) into healthcare systems is revolutionising the way medical data is processed, analysed, and utilised to improve patient outcomes. "From Data to Care: The Role of Artificial Intelligence in Modern Healthcare Systems" explores the transformative impact of AI technologies on healthcare practices, focusing on the transition from raw medical data to actionable insights for patient care. AI-driven tools, such as machine learning algorithms, natural language processing, and predictive analytics, are enabling more accurate diagnoses, personalised treatment plans, and efficient healthcare delivery. By enhancing the ability to process vast amounts of data, AI supports clinicians in making data-driven decisions, optimising workflows, and improving patient safety. This paper also addresses the challenges of implementing AI in healthcare, including data privacy concerns, regulatory hurdles, and the need for healthcare professionals to adapt to AI-enhanced environments. The potential for AI to drive innovation in preventive care, remote monitoring, and decision support systems positions it as a pivotal technology for the future of healthcare. Ultimately, AI is not just about improving clinical outcomes, but also about enhancing the overall patient experience, making healthcare systems more accessible, efficient, and effective.

Stroke is a leading cause of disability and mortality worldwide, with survivors frequently experiencing motor and cognitive impairments that hinder their daily functioning and independence. Dual-task training (DTT), an innovative rehabilitation approach, targets simultaneous improvement in motor and cognitive functions by addressing cognitive-motor interference. This narrative review evaluates the efficacy and safety of DTT in poststroke rehabilitation. PubMed, Google Scholar, Cochrane Library, Scopus, and the Directory of Open Access Journals were searched, yielding 31 studies, including randomized controlled trials and observational studies. Articles were identified that evaluated the safety and efficacy of DTT for poststroke rehabilitation in patients who had suffered from hemorrhagic or ischemic stroke. Before extracting variables, the studies were imported into Rayyan software, and thematic analysis was subsequently conducted using Microsoft Excel. The findings indicate that DTT enhances gait parameters, balance, and cognitive functions, particularly attention and executive function. Additionally, it improves functional outcomes, including activities of daily living, and reduces fall risk. While DTT demonstrates promise in fostering neuroplasticity and improving recovery outcomes, questions regarding its long-term efficacy, optimal implementation, and safety warrant further investigation. This review highlights the potential of DTT as a promising approach in stroke rehabilitation, supporting more integrated recovery strategies.

Refinement of the spinal cord injury rat model and validation of its applicability as a model for memory loss and chronic pain

A healthcare system that is transformed by cutting-edge technology, where patient outcomes improve, clinical workflows become more efficient, and the overall quality of care reaches new heights, this is what Generative Artificial Intelligence is promising in healthcare solutions. Unlike traditional AI which analyzes and processes existing data, generative AI has the unique ability to create new content including creating synthetic medical images, crafting personalized treatment plans, and developing new drug therapies. The aim of this chapter is to explore Generative AI in healthcare aligning with human rights. The focus is on healthcare systems benefiting from this technology and provides a comprehensive overview of the current applications in healthcare. Generative AI alignment with human rights principles and standards is also discussed by critically examining its adherence to privacy, equity, and non-discrimination, and evaluating the ethical implications of AI in healthcare

Artificial Intelligence (AI) is revolutionizing healthcare by enhancing patient outcomes, reducing costs, and increasing the efficiency of medical professionals. This mini-review explores the diverse applications of AI in healthcare, including disease diagnosis, personalized treatment plans, and patient survival rate predictions. AI technologies such as Machine Learning (ML), deep learning, Natural Language Processing (NLP), and Robotic Process Automation (RPA) are becoming integral to modern healthcare practices. These technologies enable early disease detection, particularly in cases like cancer, by analyzing medical images and patient data, leading to more effective and personalized treatment strategies. Additionally, AI can predict patient outcomes by analyzing large datasets from electronic health records, providing valuable insights that can inform clinical decisions. However, the integration of AI in healthcare also presents significant ethical challenges. Issues such as data privacy, algorithmic bias, lack of transparency, and the potential for increased health inequalities need to be addressed. The World Health Organization (WHO) has provided guidelines emphasizing the ethical use of AI, highlighting the importance of designing AI systems that respect human rights and promote equity. As AI continues to advance, it is crucial to ensure its responsible and transparent use to maximize benefits and minimize risks. This review underscores the transformative potential of AI in healthcare while calling for vigilant ethical considerations to ensure that AI technologies are implemented in a manner that enhances patient care and upholds ethical standards.

Background and objective: The rapid expansion of artificial intelligence (AI) in healthcare has resulted in substantial advances in diagnostics, prognostics, clinical decision support systems, and patient monitoring. Despite promising performance, many AI-based systems remain insufficiently understood by clinicians and patients due to their “black-box” nature. This lack of transparency may undermine trust, hinder acceptance, and limit safe integration into routine clinical practice. Explainable artificial intelligence (XAI) has emerged as a response to these challenges by enabling human-interpretable explanations of algorithmic decisions. The objective of this narrative review is to synthesize current evidence on XAI in healthcare, with particular emphasis on its technical foundations, clinical applications, influence on trust and decision-making, and broader social and ethical implications. Scope of review: This review synthesizes literature published between 2019 and 2025 addressing explainability in medical AI. The analysis includes methodological studies, clinical evaluations, human–computer interaction research, and social science investigations related to transparency, trust, accountability, and acceptance of AI systems. Relevant publications were identified through structured searches of PubMed, MEDLINE, Scopus, and Google Scholar using keywords related to explainable AI, interpretability, ethics, trust, and clinical decision support. Findings: XAI methods—including feature attribution, model simplification, counterfactual explanations, and visualization techniques—demonstrate potential to enhance clinician understanding of AI outputs and increase confidence in algorithm-assisted decisions. Evidence suggests that explainability may support diagnostic accuracy, reduce automation bias, and facilitate error detection. However, explainability alone does not ensure trust. Clinical context, user expertise, organizational culture, and regulatory frameworks play critical roles in shaping the adoption and appropriate use of explainable systems. Empirical research addressing patient perspectives remains limited. Conclusions: Explainable AI constitutes an important step toward the responsible and socially acceptable integration of intelligent systems in healthcare. While XAI can enhance transparency and trust, its effectiveness depends on thoughtful design, contextual adaptation to clinical workflows, and alignment with user needs. Further interdisciplinary research is required to standardize explainability approaches, evaluate their real-world impact on clinical outcomes, and address the ethical, legal, and societal challenges associated with medical AI.

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.

BackgroundArtificial intelligence (AI) has demonstrated superior diagnostic accuracy compared with medical practitioners, highlighting its growing importance in health care. SMART-Pred (Shiny Multi-Algorithm R Tool for Predictive Modeling) is an innovative AI-based application for Alzheimer disease (AD) prediction using handwriting analysis.ObjectiveThis study aimed to develop and evaluate a noninvasive, cost-effective AI tool for early AD detection, addressing the need for accessible and accurate screening methods.MethodsThe study used principal component analysis for dimensionality reduction of handwriting data, followed by training and evaluation of 10 diverse AI models, including logistic regression, naïve Bayes, random forest, adaptive boosting, support vector machine, and neural network. Model performance was assessed using accuracy, sensitivity, precision, specificity, F1-score, and area under the curve (AUC) metrics. The DARWIN (Diagnosis Alzheimer With Handwriting) dataset, comprising handwriting samples from 174 participants (89 patients with AD and 85 healthy controls), was used for validation and testing.ResultsThe neural network classifier achieved an accuracy of 91% (95% CI 0.79‐0.97) and an AUC of 94% on the test set after identifying the most significant features for AD prediction. These performance results surpass those of current clinical diagnostic tools, which typically achieve around 81% accuracy. SMART-Pred’s performance aligns with recent AI advancements in AD prediction, such as Cambridge scientists’ AI tool achieving 82% accuracy in identifying AD progression within 3 years, using cognitive tests and magnetic resonance imaging scans. The variables “air_time” and “paper_time” consistently emerged as critical predictors for AD across all 10 AI models, highlighting their potential importance in early detection and risk assessment. To augment transparency and interpretability, we incorporated the principles of explainable AI, specifically using Shapley Additive Explanations, a state-of-the-art method to emphasize the features responsible for our model’s efficacy.ConclusionsSMART-Pred offers noninvasive, cost-effective, and efficient AD prediction, demonstrating the transformative potential of AI in health care. While clinical validation is necessary to confirm the practical applicability of the identified key variables, the findings of this study contribute to the growing body of research on AI-assisted AD diagnosis and may lead to improved patient outcomes through early detection and intervention.

BackgroundAgeing is one of the most important challenges in our society. Evaluating how one is ageing is important in many aspects, from giving personalized recommendations to providing insight for long-term care eligibility. Machine learning can be utilized for that purpose, however, user reservations towards “black-box” predictions call for increased transparency and explainability of results. This study aimed to explore the potential of developing a machine learning-based healthy ageing scale that provides explainable results that could be trusted and understood by informal carers.MethodsIn this study, we used data from 696 older adults collected via personal field interviews as part of independent research. Explanatory factor analysis was used to find candidate healthy ageing aspects. For visualization of key aspects, a web annotation application was developed. Key aspects were selected by gerontologists who later used web annotation applications to evaluate healthy ageing for each older adult on a Likert scale. Logistic Regression, Decision Tree Classifier, Random Forest, KNN, SVM and XGBoost were used for multi-classification machine learning. AUC OvO, AUC OvR, F1, Precision and Recall were used for evaluation. Finally, SHAP was applied to best model predictions to make them explainable.ResultsThe experimental results show that human annotations of healthy ageing could be modelled using machine learning where among several algorithms XGBoost showed superior performance. The use of XGBoost resulted in 0.92 macro-averaged AuC OvO and 0.76 macro-averaged F1. SHAP was applied to generate local explanations for predictions and shows how each feature is influencing the prediction.ConclusionThe resulting explainable predictions make a step toward practical scale implementation into decision support systems. The development of such a decision support system that would incorporate an explainable model could reduce user reluctance towards the utilization of AI in healthcare and provide explainable and trusted insights to informal carers or healthcare providers as a basis to shape tangible actions for improving ageing. Furthermore, the cooperation with gerontology specialists throughout the process also indicates expert knowledge as integrated into the model.

Background: The integration of Artificial Intelligence (AI) and machine-assisted technologies in healthcare has significantly transformed diagnostics, treatment, and patient monitoring. AI-driven solutions, including deep learning for medical imaging, robotic-assisted surgery, and real-time patient analytics, have improved clinical decision-making and patient outcomes. However, challenges such as lack of interpretability, high implementation costs, and data security concerns hinder the full-scale adoption of these technologies. Purpose: This study aims to analyze the role of AI and robotics in modern healthcare, highlighting advancements in disease diagnosis, robotic rehabilitation, and predictive analytics. The research also identifies existing limitations and proposes methodologies to improve AI model transparency, reduce costs, and enhance real-time patient monitoring. Methods: A systematic literature review was conducted, analyzing AI-driven healthcare applications in diagnostics, robotic-assisted treatment, and predictive analytics. The study integrates Explainable AI (XAI), federated learning, Internet of Medical Things (IoMT), and blockchain-based security frameworks to propose solutions for current challenges. Comparative performance analysis of AI models and robotic frameworks was carried out to assess efficiency, cost-effectiveness, and adaptability in clinical environments. Results: Findings indicate that AI-based medical imaging improves disease detection accuracy by 92.5%, while robotic-assisted treatments enhance patient recovery rates by 78.2%. IoMT-powered real-time patient monitoring has demonstrated 85.1% efficiency in detecting early signs of critical conditions. However, challenges such as high computational costs, lack of standardized AI frameworks, and ethical concerns remain significant barriers to adoption. Conclusion: AI-driven healthcare solutions offer immense potential in improving medical diagnostics, precision surgeries, and patient monitoring. However, addressing issues related to model interpretability, cost reduction, and ethical AI deployment is crucial for broader implementation. Future research should focus on developing scalable, secure, and real-time adaptive AI-driven healthcare systems to optimize patient outcomes worldwide.

Artificial intelligence (AI) is increasingly integrated into modern healthcare, with rapidly expanding applications in medical diagnostics, laboratory medicine, rehabilitation, and patient-centered digital health solutions. The aim of this narrative review is to provide a critically curated overview of current clinical applications of AI across the healthcare continuum, from diagnosis to rehabilitation, while highlighting their clinical benefits, limitations, and implementation challenges. A targeted narrative literature search was conducted using major biomedical databases, including PubMed/MEDLINE, Scopus, Web of Science, and Embase, with emphasis on recent and influential studies published primarily over the past decade. Evidence was qualitatively synthesized across key clinical domains, including diagnostic imaging, laboratory diagnostics, rehabilitation technologies, and conversational agents. The reviewed literature indicates that AI systems can achieve diagnostic performance comparable to healthcare professionals in selected, well-defined tasks, particularly within imaging-based specialties such as radiology, mammography, ophthalmology, dermatology, and digital pathology, predominantly under retrospective or controlled study conditions. In laboratory medicine, AI-based tools support workflow optimization, result interpretation, and clinical decision support, while in rehabilitation, AI-enabled systems - including robotics, motion analysis platforms, and large language models - facilitate personalized therapy and functional recovery, albeit with heterogeneous evidence and limited prospective validation. AI-based chatbots demonstrate potential to support patient education, mental health interventions, and communication workflows, particularly as adjuncts to clinician-led care. Despite these advances, challenges related to generalizability, algorithmic bias, ethical implementation, and regulatory oversight persist. Overall, this review underscores that AI should be regarded as a supportive clinical decision-support technology rather than a replacement for healthcare professionals, with future research prioritizing prospective validation, real-world effectiveness, and responsible integration into routine clinical practice.

AI is capable of solving many problems in the contemporary healthcare systems, and its application is likely to enhance the diagnostic process, choose individual treatment plans, enhance the level of patient satisfaction and enhance the organization of the delivery of healthcare. Techniques of machine learning and natural language processing based on AI have revealed their efficiency for diagnosing the illness, getting the positive results and client’s oriented recommendations concerning treatment of diseases. Nonetheless, the advancement and use of AI technologies in healthcare brings to the table several innuendos: privacy; security; data control and dissemination; responsibility and prejudice. Luckily however, addressing these challenges needs not only good and advisable policies and guidelines but also cooperation among policy makers, ethicists, technology and health care providers /researchers etc. The purpose of this review is to identify the most common ethical concern regarding the application of AI in health care and the results of practical efforts. It escalates how AI could vary from altering diagnosis approaches, enhancing therapies, controlling documents to raising stress on consumer’s details and securing that those leveraging artificial intelligence act morally and brightly. But the process of getting there into the era of benefit realization from this new technology will open more ideas and details into risks such as bias inference in AI models, patient’s consent and agency, and accessibility of AI technologies in developing countries and remote areas. In conclusion it will be necessary to be purposely reasonable, to be conscious and very communicative in order to introduce AI into better and convenient and humane healthcare.