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Despite the tremendous promise offered by artificial intelligence (AI) for healthcare in South Africa, existing policy frameworks are inadequate for encouraging innovation in this field. Practical, concrete and solution-driven policy recommendations are needed to encourage the creation and use of AI systems. This article considers five distinct problematic issues which call for policy development: (i) outdated legislation; (ii) data and algorithmic bias; (iii) the impact on the healthcare workforce; (iv) the imposition of liability dilemma; and (v) a lack of innovation and development of AI systems for healthcare in South Africa. The adoption of a national policy framework that addresses these issues directly is imperative to ensure the uptake of AI development and deployment for healthcare in a safe, responsible and regulated manner.

Artificial Intelligence (AI), also referred to as the new electricity, is the emerging focus area in India. AI refers to the ability of machines to perform cognitive tasks like thinking, perceiving, learning, problem solving and decision making. Most of the AI systems rely on historical large datasets for predicting future trends and outcomes at a pace which humans would not be able to match. The development of AI in India is in the initial stages and there is no regulatory body focused solely on AI. However, recently, Government of India has taken various initiatives related to AI such as establishment of Artificial Intelligence Task Force, formulation of NITI Aayog's National Strategy for Artificial Intelligence #AIFORALL, setting up of four Committees for AI under Ministry of Electronics and Information technology etc. Some of India's state governments have also taken few initiatives, such as establishment of Centre of Excellence for Data Science and Artificial Intelligence (CoE-DS&AI) by Karnataka, Safe and Ethical Artificial Intelligence Policy 2020 and Face Recognition Attendance System by Tamil Nadu, AI-Powered System for monitoring driving behaviour by West Bengal, AI System to fight agricultural risks by Maharashtra etc. As with any other technology, AI brings with it a span of opportunities and challenges. In healthcare, AI could be beneficial in mining medical records; designing treatment plans; forecasting health events; assisting repetitive jobs; doing online consultations; assisting in clinical decision making; medication management; drug creation; making healthier choices and decisions; and solving public health problems etc. AI could be very helpful in areas where there is scarcity of human resources, such as rural and remote areas. AI technology has been helpful in dealing with COVID-19 in India. It has helped in preliminary screening of COVID-19 cases, containment of coronavirus, contact tracing, enforcing quarantine and social distancing, tracking of suspects, tracking the pandemic, treatment and remote monitoring of COVID-19 patients, vaccine and drug development etc. The path for adoption of AI driven healthcare in India is filled with a lot of challenges. The unstructured data sets, interoperability issues, lack of open sets of medical data, inadequate analytics solutions which could work with big data, limited funds, inadequate infrastructure, lack of manpower skilled in AI, regulatory weaknesses, inadequate framework and issues related to data protection are some of the key challenges for AI-driven healthcare. It is recommended that government should support companies to invest in AI; encourage public private partnerships in the domain of AI and Health; enact and effectively enforce laws and legislation related to AI and Health; frame policies addressing issues related to confidentiality and privacy in the AI-driven healthcare; and establish a certification system for AI-based healthcare solutions. To adopt AI-based healthcare, it is important to train workforce in AI so that they can carefully handle sensitive health information, protect data against theft and use AI systems effectively. It is also crucial that healthcare decisions based on AI solutions should have a rationale and are explainable.

Research on the issues and problems of the introduction of artificial intelligence into various spheres of life of a modern individual, the society, and the state are in the trend of scientific research at the present stage of information technology development. Artificial intelligence (AI) is being used now more than ever before, especially in the healthcare sector. Artificial intelligence has provided a more efficient way to automate routine work and other daily tasks, as well as manage patients and medical resources. The system can perform most of the tasks previously performed by humans, making it faster and cheaper. This significant advantage has facilitated the activities of interacting parties in the healthcare sector, doctors and patients. Artificial intelligence continues to gain momentum. Currently, there are modern machine learning solutions that can act, learn, understand, and predict. This is a step forward compared to robotic assistants in surgical interventions and the binding of genetic codes previously controlled by artificial intelligence. The article demonstrates that the development of artificial intelligence in healthcare involves some risks and problems — artificial intelligence systems expose patients to the risk of injury, and patient data for use in artificial intelligence systems involve the risk of privacy invasion. The article shows the advantages and disadvantages of the introduction of artificial intelligence in the field of healthcare, and the necessary conclusions are drawn.

Artificial Intelligence (AI), also referred to as the new electricity, is the emerging focus area in India. AI refers to the ability of machines to perform cognitive tasks like thinking, perceiving, learning, problem solving and decision making. Most of the AI systems rely on historical large datasets for predicting future trends and outcomes at a pace which humans would not be able to match. The development of AI in India is in the initial stages and there is no regulatory body focused solely on AI. However, recently, Government of India has taken various initiatives related to AI such as establishment of Artificial Intelligence Task Force, formulation of NITI Aayog's National Strategy for Artificial Intelligence #AIFORALL, setting up of four Committees for AI under Ministry of Electronics and Information technology etc. Some of India's state governments have also taken few initiatives, such as establishment of Centre of Excellence for Data Science and Artificial Intelligence (CoE-DS&AI) by Karnataka, Safe and Ethical Artificial Intelligence Policy 2020 and Face Recognition Attendance System by Tamil Nadu, AI-Powered System for monitoring driving behaviour by West Bengal, AI System to fight agricultural risks by Maharashtra etc. As with any other technology, AI brings with it a span of opportunities and challenges. In healthcare, AI could be beneficial in mining medical records; designing treatment plans; forecasting health events; assisting repetitive jobs; doing online consultations; assisting in clinical decision making; medication management; drug creation; making healthier choices and decisions; and solving public health problems etc. AI could be very helpful in areas where there is scarcity of human resources, such as rural and remote areas. AI technology has been helpful in dealing with COVID-19 in India. It has helped in preliminary screening of COVID-19 cases, containment of coronavirus, contact tracing, enforcing quarantine and social distancing, tracking of suspects, tracking the pandemic, treatment and remote monitoring of COVID-19 patients, vaccine and drug development etc. The path for adoption of AI driven healthcare in India is filled with a lot of challenges. The unstructured data sets, interoperability issues, lack of open sets of medical data, inadequate analytics solutions which could work with big data, limited funds, inadequate infrastructure, lack of manpower skilled in AI, regulatory weaknesses, inadequate framework and issues related to data protection are some of the key challenges for AI-driven healthcare. It is recommended that government should support companies to invest in AI; encourage public private partnerships in the domain of AI and Health; enact and effectively enforce laws and legislation related to AI and Health; frame policies addressing issues related to confidentiality and privacy in the AI-driven healthcare; and establish a certification system for AI-based healthcare solutions. To adopt AI-based healthcare, it is important to train workforce in AI so that they can carefully handle sensitive health information, protect data against theft and use AI systems effectively. It is also crucial that healthcare decisions based on AI solutions should have a rationale and are explainable.

Background Explainability is one of the most heavily debated topics when it comes to the application of artificial intelligence (AI) in healthcare. Even though AI-driven systems have been shown to outperform humans in certain analytical tasks, the lack of explainability continues to spark criticism. Yet, explainability is not a purely technological issue, instead it invokes a host of medical, legal, ethical, and societal questions that require thorough exploration. This paper provides a comprehensive assessment of the role of explainability in medical AI and makes an ethical evaluation of what explainability means for the adoption of AI-driven tools into clinical practice. Methods Taking AI-based clinical decision support systems as a case in point, we adopted a multidisciplinary approach to analyze the relevance of explainability for medical AI from the technological, legal, medical, and patient perspectives. Drawing on the findings of this conceptual analysis, we then conducted an ethical assessment using the "Principles of Biomedical Ethics" by Beauchamp and Childress (autonomy, beneficence, nonmaleficence, and justice) as an analytical framework to determine the need for explainability in medical AI. Results Each of the domains highlights a different set of core considerations and values that are relevant for understanding the role of explainability in clinical practice. From the technological point of view, explainability has to be considered both in terms how it can be achieved and what is beneficial from a development perspective. When looking at the legal perspective we identified informed consent, certification and approval as medical devices, and liability as core touchpoints for explainability. Both the medical and patient perspectives emphasize the importance of considering the interplay between human actors and medical AI. We conclude that omitting explainability in clinical decision support systems poses a threat to core ethical values in medicine and may have detrimental consequences for individual and public health. Conclusions To ensure that medical AI lives up to its promises, there is a need to sensitize developers, healthcare professionals, and legislators to the challenges and limitations of opaque algorithms in medical AI and to foster multidisciplinary collaboration moving forward. ; ISSN:1472-6947

Sustainable rural development involves aholistic approach where daily basic needs of rural populations mustbe covered by reliable public utilities combined with technical, socioeconomic, and environmental conditions to support regional economies and urban-rural linkages. Sustainable rural developmentis vital to the economic, social and environmental viability of nations. It is essential for poverty eradication since global poverty is overwhelmingly rural. Today's era is considered as an age of science, technology, communication, intelligence, education and economy. The human being is tried to develop the society by implementing and adapting these concepts, especially in villages, city and town and turn them into Smart Village, Smart city and Smart Town. So, the objective of this report is to discuss role and the impact of artificial intelligence in rural areas for the development purposes, which is also known as villages. This paper puts light on how to develop the rural areas by implementing various technology related to AI and ML. In this paper we are going to consider some main areas which plays major role in sustainable rural development which are as education, agri-sector, healthcare system, e-commerce and connectivity. Artificial intelligence is considered the future of technology and digitalization. Its capacity to contribute to almost every sector of the industry offers many possibilities for governments and society to grow. That is why, many businesses around the globe have incorporated this technology into their procedures, products and services. The pursuit of this technological development has reached governments. Because of that, they are now trying to implement Artificial Intelligence in fields likehealthcare, education, economy or agriculture. There is no doubt AI will be an essential foundation in the future of every country.

Artificial Intellegence is an assemblage of many algorithms for analysing and interpreting knowledge from vast collection of heterogenous data, which influenced a wide range of indusries. The concepts of AI are related to fields like statistics, probability, pattern recognition, machie learning etc. collectively called as "computational intelligence". This paper analyses the impact of these techinques in the healthcare life cycle starting from diagnosis to treatment and also its contribution in prevention. As a small error in the applicability will lead to dangeous and ireversible effect, this paper analyses the means by which governments taking care of and ensuring their performance while giving permission for the products based on AI in healthcare.

Intro -- Preface -- Need for a Book on the Proposed Topics -- Organization of the Book -- Audience -- Contents -- Contributors -- Acronyms -- 1 Artificial Intelligence for Healthcare Logistics: An Overview and Research Agenda -- 1.1 Introduction -- 1.2 Machine Learning and Artificial Intelligence -- 1.2.1 Machine Learning -- 1.2.2 Artificial Intelligence -- 1.2.3 Working Definition -- 1.3 Framework for Healthcare Logistics Literature -- 1.3.1 Planning Levels -- 1.3.2 Care Levels -- 1.3.3 User Types -- 1.3.4 Framework -- 1.4 Literature Review -- 1.4.1 AI for Optimisation Input -- 1.4.2 AI for Healthcare Logistics Optimisation -- 1.4.3 AI for ED Logistics -- 1.4.4 Synthesis and Research Agenda -- 1.5 Conclusion -- References -- 2 AI/OR Synergies of Process Mining with Optimal Planning of Patient Pathways for Effective Hospital-Wide Decision Support -- 2.1 Motivation and Research Outline -- 2.1.1 AI/OR Synergies meet Hospital Decision Task Complexities -- 2.1.2 Pathway Centered Decision Support Toward AI/OR Synergy -- 2.1.3 Research on AI/OR Synergy and Chapter Outline -- 2.2 First Type of AI/OR Synergy: Process Mining of Pathways for Accurate Prescriptive Planning of Ward-and-Bed Allocation -- 2.2.1 Synergy between Predictive and Prescriptive Analytics: Cases of Simple vs. Complex Structures -- 2.2.2 First Type of AI/OR Synergy and Its Benefits for Effective Hospital Decision Support: Case Study of a University Hospital -- 2.3 Detecting AI/OR Synergies Within Hospital Decision Support: Interdependencies, Dimensions of Complexity, Two-Dimensional Scheme, and Types of AI/OR Synergy -- 2.3.1 Types of Interdependencies: First Group -- 2.3.2 Dimensions of Complexity and Overview About OR and AI Tasks and Synergies.

Artificial Intelligence-Based System Models in Healthcare provides a comprehensive and insightful guide to the transformative applications of AI in the healthcare system. This book is a groundbreaking exploration of the synergies between artificial intelligence and healthcare innovation. In an era where technological advancements are reshaping the landscape of medical practices, this book provides a comprehensive and insightful guide to the transformative applications of AI in healthcare systems. From conceptual foundations to practical implementations, the book serves as a roadmap for understanding the intricate relationships between AI-based system models and the evolution of healthcare delivery. The first section delves into the fundamental role of technology in reshaping the healthcare landscape. With a focus on daily life activities, decision support systems, vision-based management, and semantic frameworks, this section lays the groundwork for understanding the pivotal role of AI in revolutionizing traditional healthcare approaches. Each chapter offers a unique perspective, emphasizing the intricate integration of technology into healthcare ecosystems. The second section takes a deep dive into specific applications of AI, ranging from predictive analysis and machine learning to deep learning, image analysis, and biomedical text processing. With a focus on decision-making support systems, this section aims to demystify the complex world of AI algorithms in healthcare, offering valuable insights into their practical implications and potential impact on patient outcomes. The final section addresses the modernization of healthcare practices and envisions the future landscape of AI applications. From medical imaging and diagnostics to predicting ventilation needs in intensive care units, modernizing health record maintenance, natural language processing, chatbots for medical inquiries, secured health insurance management, and glimpses into the future, the book concludes by exploring the frontiers of AI-driven healthcare innovations. Audience This book is intended for researchers and postgraduate students in artificial intelligence and the biomedical and healthcare sectors. Medical administrators, policymakers and regulatory specialists will also have an interest

Artificial intelligence (AI) tools in genetics
Vessela Kristensen and Dag Undlien uncover AI tools in genetics, from variant recognition to clinical implementation. Most people are curious about how their bodies work (and the ways they occasionally do not). This curiosity extends towards how our bodies are built, their functions, and what maintains life and health. Most people think that science is remote from the lives they lead, and the decisions that they make day by day, but this is far from the truth. Our understanding of genetics may affect our choices at our doctor's office about our healthcare and reproductive decisions, including family planning.

AbstractThis paper explores the status of Artificial Intelligence (AI) for healthcare research in Africa. The aim was to use bibliometric and thematic analysis methods to determine the publication counts, leading authors, top journals and publishers, most active institutions and countries, most cited institutions, funding bodies, top subject areas, co-occurrence of keywords and co-authorship. Bibliographic data were collected on April 9 2022, through the Lens database, based on the critical areas of authorship studies, such as authorship pattern, number of authors, etc. The findings showed that several channels were used to disseminate the publications, including articles, conference papers, reviews, and others. Publications on computer science topped the list of documented subject categories. The Annals of Tropical Medicine and Public Health is the top journal, where articles on AI have been published. One of the top nations that published AI research was the United Kingdom. With 143 publications, Harvard University was the higher education institution that produced the most in terms of affiliation. It was discovered that the Medical Research Council was one of the funding organizations that supported research, resulting in the publication of articles in AI. By summarizing the current research themes and trends, this work serves as a valuable resource for researchers, practitioners, and funding organizations interested in Artificial intelligence for healthcare research in Africa.

AbstractArtificial intelligence (AI) offers much promise for improving healthcare. However, it runs the looming risk of causing individual and societal harms; for instance, exacerbating inequalities amongst minority groups, or enabling compromises in the confidentiality of patients' sensitive data. As such, there is an expanding, unmet need for ensuring AI for healthcare is developed in concordance with human values and ethics. Augmenting "principle-based" guidance that highlight adherence to ethical ideals (without necessarily offering translation into actionable practices), we offer a solution-based framework for operationalising ethics in AI for healthcare. Our framework is built from a scoping review of existing solutions of ethical AI guidelines, frameworks and technical solutions to address human values such as self-direction in healthcare. Our view spans the entire length of the AI lifecycle: data management, model development, deployment and monitoring. Our focus in this paper is to collate actionable solutions (whether technical or non-technical in nature), which can be steps that enable and empower developers in their daily practice to ensuring ethical practices in the broader picture. Our framework is intended to be adopted by AI developers, with recommendations that are accessible and driven by the existing literature. We endorse the recognised need for 'ethical AI checklists' co-designed with health AI practitioners, which could further operationalise the technical solutions we have collated. Since the risks to health and wellbeing are so large, we believe a proactive approach is necessary for ensuring human values and ethics are appropriately respected in AI for healthcare.