RapidAI for Stroke Detection and AI Implementation Review

The integration of artificial intelligence (AI) in healthcare presents significant promise to enhance clinical procedures and patient outcomes. This research examines the setting, methodology, conclusions, and issues associated with AI in healthcare. The swift proliferation of digital health data, encompassing medical imaging and clinical records, has generated substantial prospects for AI applications. Artificial intelligence methodologies, including machine learning, natural language processing, and computer vision, facilitate the derivation of significant insights from intricate datasets, hence improving clinical decision-making. A thorough literature review examines the practical applications of AI, encompassing its roles in medical diagnostics, treatment planning, and patient outcome prediction. The report also examines ethical issues, data protection, and legal frameworks, which are crucial for the responsible application of AI in healthcare. The results illustrate AI's capacity to enhance diagnostic precision, facilitate administrative efficiency, and optimise resource distribution, resulting in tailored therapies and improved healthcare administration. Nonetheless, obstacles persist, such as data integrity, algorithm transparency, and ethical considerations, which must be resolved to guarantee the secure and efficient deployment of AI. Continuous research, cooperation between healthcare and AI experts, and the establishment of comprehensive regulatory frameworks are essential for optimising the advantages of AI while minimising hazards. This research highlights AI's capacity to transform healthcare, stressing the necessity for a multidisciplinary strategy to effectively harness its benefits and tackle the associated ethical and regulatory dilemmas.

The Value of Artificial Intelligence for Healthcare Decision Making—Lessons Learned

The convergence of healthcare with the Internet of Things (IoT) and Artificial Intelligence (AI) is reshaping medical practice with promising enhanced data-driven insights, automated decision-making, and remote patient monitoring. It has the transformative potential of these technologies to revolutionize diagnosis, treatment, and patient care. This study aims to explore the integration of IoT and AI in healthcare, outlining their applications, benefits, challenges, and potential risks. By synthesizing existing literature, this study aims to provide insights into the current landscape of AI, IoT, and AIoT in healthcare, identify areas for future research and development, and establish a framework for the effective use of AI in health. A comprehensive literature review included indexed databases such as PubMed/Medline, Scopus, and Google Scholar. Key search terms related to IoT, AI, healthcare, and medicine were employed to identify relevant studies. Papers were screened based on their relevance to the specified themes, and eventually, a selected number of papers were methodically chosen for this review. The integration of IoT and AI in healthcare offers significant advancements, including remote patient monitoring, personalized medicine, and operational efficiency. Wearable sensors, cloud-based data storage, and AI-driven algorithms enable real-time data collection, disease diagnosis, and treatment planning. However, challenges such as data privacy, algorithmic bias, and regulatory compliance must be addressed to ensure responsible deployment of these technologies. Integrating IoT and AI in healthcare holds immense promise for improving patient outcomes and optimizing healthcare delivery. Despite challenges such as data privacy concerns and algorithmic biases, the transformative potential of these technologies cannot be overstated. Clear governance frameworks, transparent AI decision-making processes, and ethical considerations are essential to mitigate risks and harness the full benefits of IoT and AI in healthcare.

The digitalisation of social and healthcare services is progressing rapidly in Finland. A wide range of digital health technologies (DHTs) are already in use or will be implemented soon, including digital lifestyle interventions, digital therapeutics (DTx), digital home care solutions, and digital service centres for citizens. The use of innovative technologies like artificial intelligence (AI) is expanding, and robotics is already applied in areas such as rehabilitation and medicine dispensing in home care. Assessing the diverse value of DHTs has posed a challenge. To address this, the Finnish Ministry of Social Affairs and Health commissioned the University of Oulu and Finnish Coordinating Center for Health Technology Assessment (FinCCHTA) to develop a new health technology assessment (HTA) model. The result, Digi-HTA, was launched in late 2019 and integrated into FinCCHTA's routine operations since then. It was the first framework to systematically assess a wide range of DHTs-such as health apps, digital home care solutions, AI solutions, and robotics-within a single structure. Digi-HTA is already being used by Finland's wellbeing services counties to support decisions and procurements. Additionally, a reimbursement pilot for digital health applications will be launched during the current government term, requiring approved apps to pass a Digi-HTA assessment. DHTs’ value is highly dependent on their context of use-they are tools that can help reshape care processes and potentially provide benefits for multiple stakeholders simultaneously, such as improving citizens’ health and increasing the efficiency of service delivery. HTA for DHTs demands new considerations beyond traditional HTA domains like effectiveness, cost, and safety. Aspects such as cybersecurity, usability, and accessibility for all intended users are essential. The use of AI in healthcare also introduces new HTA perspectives that have not been considered in previous HTA methodologies

A study on the regulation of artificial intelligence (AI) in healthcare, includes a brief overview of the current state of use of AI in healthcare and its potential benefits and risks. The article summarizes the current regulations that exist for AI in healthcare, including any relevant laws, guidelines, and best practices, including information on regulatory bodies such as the FDA and HIPAA. The ethical considerations arising from the use of AI in healthcare, such as patient confidentiality and data security, bias in algorithms, and transparency in decision making, are given. Examples of AI in healthcare are given that illustrate the challenges and opportunities provided by the technology, including both successful and unsuccessful implementations. Future developments in AI and healthcare are described, including emerging technologies and trends, and predictions of how rules might evolve in response to these developments. Summarize and provide recommendations for addressing regulatory challenges related to AI in healthcare.

Artificial Intelligence (AI) is revolutionizing healthcare through its integration into various domains, significantly enhancing the efficiency, accuracy, and effectiveness of medical practices. This review explores the transformative impact of AI across multiple aspects of healthcare, including diagnostics, personalized treatment, drug discovery, surgery, and more. AI's capabilities in diagnostics and early detection are improving the precision and speed of disease identification, enabling earlier and more effective interventions. Personalized treatment approaches leverage AI to analyze patient data and tailor therapies to individual needs, optimizing outcomes and reducing adverse effects. AI-driven robotics in surgery offer enhanced precision, control, and minimally invasive options, leading to improved surgical outcomes and faster recovery times. Despite these advancements, the adoption of AI in healthcare presents challenges and ethical considerations, including data quality, algorithmic bias, patient privacy, and the responsible use of AI technologies. Addressing these issues is crucial for maintaining trust and ensuring equitable access to AI-powered healthcare solutions. AI's role in drug discovery and development is accelerating the creation of new therapies by optimizing predictive modeling, drug design, and clinical trials, thus reducing costs and speeding up the development process. Future trends and innovations in AI highlight ongoing advancements and the potential for further transformation in healthcare. These include advancements in natural language processing, AI-enhanced telemedicine, wearable health technologies, and ethical AI governance. As AI technology continues to evolve, its impact on healthcare will become increasingly significant, driving progress in patient care, operational efficiency, and medical research. Collaborative efforts among technologists, clinicians, researchers, and policymakers will be essential in harnessing AI's full potential while addressing the complexities and ethical challenges associated with its use. This review underscores the promise of AI to revolutionize healthcare and improve patient outcomes while emphasizing the need for responsible implementation and ongoing evaluation.

As Artificial Intelligence (AI) continues to play an increasingly pivotal role in medical decision support systems, the ethical implications of its integration into healthcare practices demand comprehensive examination. This review delves into the ethical considerations surrounding AI-enhanced medical decision support systems, aiming to provide insights into the challenges, existing frameworks, exemplary practices, and emerging trends in this rapidly evolving field. The significance of ethical considerations is underscored by the patient-centric focus, emphasizing the impact of AI on patient outcomes and the delicate balance between technological advancements and patient welfare. Trust and transparency emerge as critical pillars, exploring the role of trust in medical decision-making and the imperative of ensuring transparency in AI algorithms to foster confidence among healthcare professionals and patients. Ethical challenges, including privacy and confidentiality concerns, biases in AI algorithms, and issues related to informed consent, are thoroughly examined. Strategies for safeguarding patient data, mitigating biases, and transparently communicating with patients are explored to address these challenges. The role of accountability and responsibility is delineated, defining the ethical responsibilities of both healthcare professionals and AI developers. The review surveys existing ethical frameworks in healthcare AI and evaluates their applicability and effectiveness. Additionally, it highlights recent proposals for ethical guidelines, emphasizing the need to integrate ethical considerations into the entire development life cycle of AI-enhanced medical decision support systems. Case studies and exemplary practices from healthcare institutions implementing ethical AI serve to illustrate real-world applications and offer insights into best practices. The evolving landscape of ethical AI research is explored, showcasing ongoing initiatives and potential innovations that hold promise for addressing ethical challenges in the future. This review underscores the paramount importance of ethical considerations in the integration of AI into medical decision support systems. It provides a comprehensive overview of current challenges, existing frameworks, exemplary practices, and emerging trends, emphasizing the ongoing need for vigilance and ethical governance to ensure the responsible and beneficial deployment of AI in healthcare.

Abstract: This chapter delves into the transformative role of Artificial Intelligence (AI) in healthcare, highlighting its potential to revolutionize patient care, diagnostics, and healthcare operations. AI empowers healthcare professionals to make more informed decisions, optimizes patient outcomes, and streamlines administrative processes. The chapter explores the impact of AI across various domains, including diagnostics, patient monitoring, treatment personalization, and operational efficiency, while also addressing the ethical considerations and challenges inherent in deploying AI solutions, such as data privacy, consent, and bias. As AI continues to advance, its potential to improve healthcare outcomes and accessibility is immense, provided that innovation is guided by ethical considerations and a collaborative approach among technologists, healthcare professionals, and policymakers. This chapter underscores the importance of continuous innovation, ethical practices, and collaboration to fully realize AI's benefits in healthcare, setting the stage for a future where AI-driven healthcare solutions are both transformative and responsibly implemented. Keywords: Artificial Intelligence (AI),Healthcare,Patient Care,Diagnostics,Personalized Medicine,Machine Learning,Deep Learning,Natural Language Processing (NLP),Healthcare Operations,Ethical Considerations,Data Privacy,Treatment Personalization,Remote Monitoring,Clinical Decision Support and Healthcare Innovation.novation.

In recent years, the integration of artificial intelligence (AI) in healthcare has led to numerous groundbreaking applications that have transformed various aspects of medical practice. One of the primary areas where AI has made substantial contributions is in medical imaging analysis. By leveraging machine learning algorithms, AI systems can assist radiologists in interpreting medical images with greater accuracy and efficiency. AI-driven tools can detect subtle abnormalities, aid in early disease detection, and facilitate more precise diagnosis and treatment planning. Predictive analytics is another key application of AI in healthcare, wherein algorithms analyze vast amounts of patient data to forecast potential health outcomes and identify individuals at high risk of developing certain conditions. Additionally, the rise of virtual health assistants powered by AI has revolutionized patient care delivery by providing personalized and accessible healthcare services. These virtual assistants, often in the form of chatbots or voice-enabled interfaces, can interact with patients, answer medical queries, schedule appointments, and even provide medication reminders. Overall, the various applications of AI in healthcare, including medical imaging analysis, predictive analytics, personalized medicine, and virtual health assistants, have demonstrated significant potential in improving diagnostic accuracy, optimizing treatment plans, and enhancing patient care delivery. As these technologies continue to evolve and mature, they have the potential to revolutionize healthcare delivery and contribute to better health outcomes for individuals worldwide. This research paper contributes to the ongoing discourse surrounding the integration of AI in healthcare by providing a comprehensive overview of its advancements, challenges, and ethical considerations.

Recent developments in the application of artificial intelligence (AI) in health care promise to solve many of the existing global problems in improving human health care and managing global legal challenges. In addition to machine learning techniques, artificial intelligence is currently being applied in health care in other forms, such as robotic systems. However, the artificial intelligence currently used in health care is not fully autonomous, given that health care professionals make the final decision. Therefore, the most prevalent legal issues relating to the application of artificial intelligence are patient safety, impact on patient-physician relationship, physician’s responsibility, the right to privacy, data protection, intellectual property protection, lack of proper regulation, algorithmic transparency and governance of artificial intelligence empowered health care. Hence, the aim of this research is to point out the possible legal consequences and challenges of regulation and control in the application of artificial intelligence in health care. The results of this paper confirm the potential of artificial intelligence to noticeably improve patient care and advance medical research, but the shortcomings of its implementation relate to a complex legal and ethical issue that remains to be resolved. In this regard, it is necessary to achieve a broad social consensus regarding the application of artificial intelligence in health care, and adopt legal frameworks that determine the conditions for its application.

IntroductionThe ever-increasing number of new and innovative digital health technologies (DHTs) also sets new demands on health technology assessment (HTA) methods in addition to traditional HTA domains. In 2018, the Finnish Ministry of Social Affairs and Health recognized the need for new HTA methods for DHTs in Finland and commissioned method development.MethodsThe development work of the new HTA method for DHTs and the findings related to it were based on three substudies:(i) The new HTA method was developed through a literature review, expert interviews, and four multiprofessional workshops.(ii) Feedback about new HTA recommendations was collected from healthcare decision-makers through a web-based survey (n=24). Feedback on the developed HTA framework was collected through a web-based survey for companies offering DHT products (n=8).(iii) Initial experiences about the state of data security and protection of assessed products were gathered through the assessment process.ResultsA new Digi-HTA method that supports a wide range of DHTs, such as health apps, AgeTech, artificial intelligence, and robotic solutions, was published in 2019. According to the healthcare decision-makers participating in the study, although the Digi-HTA recommendations included clear and beneficial information, their integration into healthcare decision-making processes should be improved. Responses from companies offering different DHTs indicated that the Digi-HTA framework would be an appropriate tool for performing assessments for their products. During the assessments, deficiencies in compliance with the best practices of data security and protection as well as data security problems were found.ConclusionsThe rapid development of DHTs requires that the HTA methods also adapt to the development so that no new and innovative products are excluded from the assessments. In addition to the value of DHTs, their quality, such as data security and protection, should be assessed so that decision-makers can be supported in the best possible ways.

Fast paced globalization engendered the requirement of smart and rapid healthcare solutions. These developments have significantly enhanced the diagnosis, management, and treatment of diseases, leading to better patient outcomes and a higher standard of living. Computer programs created to carry out operations like learning, problem-solving, and decision-making that normally demand for human intelligence are referred to as artificial intelligence. It includes a broad range of tools and methods, such as natural language processing, deep learning, and machine learning. A formidable and innovative field of computer science, known as artificial intelligence (AI) has the potential to drastically alter medical practice and healthcare delivery. Topics covered in this book include the latest developments in the use of AI in healthcare. This book also outlines a roadmap for developing safe, dependable, and effective AI systems as well as potential future directions for AI-enhanced healthcare systems. The fundamentals of AI, its significant technologies and its applications in various healthcare sectors have been discussed in chapter 1. The prominent role of AI in medical imaging and diagnosis has been described in chapter 2. Image processing and computer vision plays a vital role in early prediction and detection of disease like cancer, mental and neurological disorders etc. Chapter 3 illustrate how AI can improve drug discovery and development process. AI can also accelerate drug design and screening, pharmaceutical product development, clinical trials as well as quality control. Chapter 4 focuses on significance of AI in virtual healthcare like virtual health assistance, telemedicine, AI chatbots and personalised healthcare. Rapidly transforming surgical practice through robotics and AI with its ethical considerations are described in chapter 5. Chapter 6 demonstrate how complex healthcare management systems are transformed to smart, automated and easy to use functionalities with the use of AI. Ethical Regulations, Limitations and Future Directions of AI in Healthcare have been discussed in chapter 7. Finally, conclusion illustrate the overall importance and innovations of AI in various healthcare domains.

The use of Artificial Intelligence (AI) in healthcare is growing quickly and offers big improvements in medical diagnostics, treatment planning, and patient care. However, people often don't trust AI systems, which prevents them from being widely used. This article looks at both the philosophical and practical issues of trust in healthcare AI systems. First, we provide an overview of the current state of AI in healthcare. Then, we review existing research on trust in technology. Based on our findings, we identify three main factors that affect trust in AI: Technology-Related Factors (transparency, reliability, safety), Healthcare Context Factors (how well AI fits into healthcare settings, proper training for professionals), and Individual User Factors (user experience and attitudes toward AI). Our results show that continuous human oversight, strong regulations, and ethical considerations are essential. Addressing these areas is key to making sure AI systems in healthcare are reliable, transparent, and trusted by both healthcare professionals and patients.

Informed consent is fundamental to ethical medical practice, ensuring that patients understand the procedures they undergo, the associated risks, and available alternatives. The advent of artificial intelligence (AI) in healthcare, particularly in diagnostics, introduces complexities that traditional informed consent forms do not adequately address. AI technologies, such as image analysis and decision-support systems, offer significant benefits but also raise ethical, legal, and practical concerns regarding patient information and autonomy. The integration of AI in healthcare diagnostics necessitates a re-evaluation of current informed consent practices to ensure that patients are fully aware of AI's role, capabilities, and limitations in their care. Existing standards, such as those in the UK's National Health Service and the US, highlight the need for transparency and patient understanding but often fall short when applied to AI. The "black box" phenomenon, where the inner workings of AI systems are not transparent, poses a significant challenge. This lack of transparency can lead to over-reliance or distrust in AI tools by clinicians and patients alike. Additionally, the current informed consent process often fails to provide detailed explanations about AI algorithms, the data they use, and inherent biases. There is also a notable gap in the training and education of healthcare professionals on AI technologies, which impacts their ability to communicate effectively with patients. Ethical and legal considerations, including data privacy and algorithmic fairness, are frequently inadequately addressed in consent forms. Furthermore, integrating AI into clinical workflows presents practical challenges that require careful planning and robust support systems. This review proposes strategies for redesigning informed consent forms. These include using plain language, visual aids, and personalised information to improve patient understanding and trust. Implementing continuous monitoring and feedback mechanisms can ensure the ongoing effectiveness of these forms. Future research should focus on developing comprehensive regulatory frameworks and enhancing communication techniques to convey complex AI concepts to patients. By improving informed consent practices, we can uphold ethical standards, foster patient trust, and support the responsible integration of AI in healthcare, ultimately benefiting both patients and healthcare providers.

BackgroundThe 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?MethodsAn 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.ResultsThe 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 < .001) and across continents (r = 0.301 to 0.531, all P < .001), with generally small effect sizes.ConclusionsThis 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).