Artificial Intelligence in the Healthcare Sector

Deleted Journal, 2024

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.

Journal of Hospital Management and Health Policy

HAPSc Policy Briefs Series, 2020

The latest developments in artificial intelligence (AI)-a general-purpose technology impacting many industries-have been based on advancements in machine learning, which is recast as a quality-adjusted decline in forecasting ratio. The influence of Policy on AI and big data has impacted two key magnitudes which are known as diffusion and consequences. And these must be focused primarily on the context of AI and big data. First, in addition to the policies on subsidies and intellectual property (IP) that will affect the propagation of AI in ways close to their effect on other technologies, three policy categories-privacy, exchange, and liability-may have a specific impact on the diffusion of AI. The first step in the prohibition process is to identify the shortcomings of current hospital procedures, why we need acute care AI, and eventually how the direction of patient decision-making will shift with the introduction of AI-based research. The second step is to establish a plan to shift the direction of medical education in order to enable physicians to retain control of AI. Medical research would need to rely less on threshold decision-making and more on the prediction, interpretation, and pathophysiological context of contextual time cycles. This should be an early part of a medical student's education, and this is what their hospital aid (AI) ought to do. Effective contact between human and artificial intelligence includes a shared pattern of focused knowledge base. Human-to-human contact protection in hospitals should lead this professional transformation process.

International Journal of Environmental Research and Public Health

Artificial intelligence and automation are topics dominating global discussions on the future of professional employment, societal change, and economic performance. In this paper, we describe fundamental concepts underlying AI and Big Data and their significance to public health. We highlight issues involved and describe the potential impacts and challenges to medical professionals and diagnosticians. The possible benefits of advanced data analytics and machine learning are described in the context of recently reported research. Problems are identified and discussed with respect to ethical issues and the future roles of professionals and specialists in the age of artificial intelligence.

Journal of Hospital Management and Health Policy, 2021

Precision medicine aims to integrate an individual's unique features from clinical phenotypes and biological information obtained from imaging to laboratory tests and health records, to arrive at a tailored diagnostic or therapeutic solution. The premise that precision medicine will reduce disease-related health and financial burden is theoretically sound, but its realisation in clinical practice is still nascent. In contrast to conventional medicine, developing precision medicine solutions is highly data-intensive and to accelerate this effort there are initiatives to collect vast amounts of clinical and biomedical data. Over the last decade, artificial intelligence (AI), which includes machine learning (ML), has demonstrated unparalleled success in pattern recognition from big data in a range of domains from shopping recommendation to image classification. It is not surprising that ML is being considered as the critical technology that can transform big data from biobanks and electronic health records (EHRs) into clinically applicable precision medicine tools at the bedside. Distillation of high-dimensional data across clinical, biological, patient-generated and environmental domains using ML and translating garnered insights into clinical practice requires not only extant algorithms but also additional development of newer methods and tools. In this review, we provide a broad overview of the prospects and potential for AI in precision medicine and discuss some of the challenges and evolving solutions that are revolutionising healthcare.

The American Journal of Medicine, 2020

Journal of Electronics, Electromedical Engineering, and Medical Informatics, 2022

Andrew NG, a leading philosopher in the field of Artificial Intelligence (AI) once quoted “AI is the new electricity” which has the potential to transform and drive every industry. The most important driving factor for the AI transformation will be data. Clive Humby, a data science entrepreneur was once quoted saying “data is the new oil” and data analytics being the “combustion engine” will drive the AI led innovations. The rapid rise of Artificial Intelligence technologies in the past decade, has inspired industries to invest in every opportunity for integrating AI solutions to their products. Research, development, and innovation in the field of AI are shaping various industries like automobile, manufacturing, finance, retail, supply chain management, and education among others. The healthcare industry has also been adopting the ways of AI into various workflows within the domain. With the evolution in computing and processing powers coupled with hardware modernizations, the adop...

Open access journal of applied science and technology, 2024

The twenty-first century has witnessed significant advancements in informatics, reshaping our understanding of data processing and accessibility. Artificial intelligence (AI), encompassing techniques such as machine learning (ML), deep learning (DP), and neural networks (NN), is poised to revolutionize medicine. AI holds the capability of analyzing vast amounts of data, extracting meaningful insights, and making accurate predictions, thereby empowering industries to make informed decisions, drive innovation, and enhance efficiency. The landscape of medical AI has evolved significantly, demonstrating expert-level disease detection from medical images and promising breakthroughs across various industries. AI revolutionizes medical practice by leveraging advanced algorithms and machine learning capabilities to improve diagnostics, treatment planning, and overall patient care. However, the deployment of medical AI systems in regular clinical practice still needs to be tapped, presenting complex ethical, technical, and human-centered challenges that must be addressed for successful implementation. While AI algorithms have shown efficacy in retrospective medical investigations, their translation into practical medical settings has been limited, raising concerns about their usability and interaction with healthcare professionals. Moreover, the representativeness of retrospective datasets in real-world medical practice is subject to filtering and cleaning biases. Integrating AI into clinical medicine holds great promise for transforming healthcare delivery, improving patient care, and revolutionizing aspects such as diagnosis, treatment planning, drug discovery, personalized treatment, and medical imaging. With advanced algorithms and machine learning capabilities, AI and robotics in Healthcare can analyze large volumes of medical data, extract meaningful insights, and provide accurate predictions, empowering healthcare professionals to make informed decisions and optimize resource allocation. The availability of extensive clinical, genomics, and digital imaging data, coupled with investments from healthcare institutions and technology giants, underscores the potential of AI in healthcare. This review article explores AI's powerful potential to revolutionize healthcare delivery across multiple domains, emphasizing the need to overcome challenges and harness its transformative capabilities in clinical practice.

International Journal of Information Technology, 2018

Artificial intelligence and technological advancements are exceptionally influenced the entire society and mankind. Unprecendented and extensive use of social media, mobile phones and the internet has resulted in accumulation of huge amount of data. Most of this big data are available in unstructured form and it is beyond the capability of traditional systems to manage, maintain, supervise, keeping and analyse the data within a limited time span. Effective analysis and interpretation of health care data provides new insights in the condition of patients and suggest the most appropriate treatment opportunities. Discovery and invention of vital information in medical data helps the health care professionals to arrive at appropriate clinical decisions and improvement of quality of life in a variety of patients. In this article, we have discussed various issues and addressed them with the updated information on big data sources, big data management, big data processing and big data analysis through various tools and techniques. We have also analysed and interpreted the recent applications and advancements in artificial intelligence and big data in the health care technology and m-Health domain.

The rise of artificial intelligence has brought a positive shift in the sector by providing accurate data-driven decisions. The data from large systems is used for the early detection of chronic illnesses. These illnesses include cancer, diabetes, and cardiovascular diseases, etc. Existing technology is limited in terms of medical diagnosis etc. With the advent of ML/AI in the healthcare system, we expect to see much automation in clinical decision-making. We illustrate popular machine learning algorithms, their applications followed by methodology. This research will focus on the impact of Artificial Intelligence applications on the healthcare sector, its history, challenges, and concerns in the medical field. BACKGROUND INFORMATION Fostering trust in AI systems is a tremendous obstacle to bringing the most transformative AI technologies into reality, such as large-scale integration of machine intelligence in medicine. The challenge is to implement guiding ethical principles and aspirations and make the responsible practice of AI accessible, reproducible, and achievable for all who engage with the AI system. Meeting this challenge is critical to ensuring that medical professionals are prepared to correctly leverage AI in their practice and, ultimately, save lives. This research will concentrate on the influence AI applications have on the healthcare sector, its need, and its history in the medical field. Artificial intelligence models will assist doctors in various applications like patient care and administrative operations. (2011, March) Plant, R. et. al. According to the National Academies of Science, Engineering-diagnostic mistakes lead to roughly 10% of patient fatalities and 6 to 17% of hospital problems. It's crucial to remember that diagnostic errors aren't always caused by poor physician performance. Diagnostic mistakes, according to experts, are caused by a number of causes, including: • Collaboration and integration of health information technology are inefficient (Health IT) • Communication breakdowns between physicians, patients, and their families • A healthcare work system that is designed to be insufficiently supportive of diagnostic procedures. LITERATURE REVIEW Machine learning is being increasingly and frequently utilized in the healthcare field in various ways, like automating medical billing, clinical decision support, and establishing clinical care standards. Friedman, C., & Elhadad, N. (2014) et al. There are several significant applications of machine learning and healthcare ideas in medicine. The first medical machine learning system to diagnose acute toxicities in patients getting radiation treatment for head and neck malignancies has been created by researchers. In radiology, deep learning in healthcare automatically detects complicated patterns and assists radiologists in making informed judgments when analyzing pictures such as traditional radiography, CT, MRI, PET scans, and radiology reports. Machine learning-based automated detection and diagnostic systems have been demonstrated to perform as well as an expert radiologist. Google is creating a machine learning platform to identify breast cancer from images. These are only a handful of the numerous applications of machine learning in healthcare Jack Jr, C(2013) et.al. Natural Language Processing Nearly 80% of the information kept or "locked" in electronic health record systems is unstructured healthcare data for machine learning. These are papers or text files, not data components that could not previously be evaluated without a human viewing the information. Unfortunately, human language, often known as "natural language," is extremely complicated, lacks consistency, and contains many ambiguities, jargon, and vagueness. Therefore, machine learning in healthcare frequently uses natural language processing (NLP) tools to transform these papers into more usable and analyzable data.

IAEME PUBLICATION, 2021

The use of AI and machine learning has great potential to revolutionise medical diagnosis and treatment. This study analyses the potential of various instruments in healthcare by comparing their advantages, disadvantages, and uses. Machine learning systems have the ability to detect patterns, improve the accuracy of diagnoses, and bolster expert opinion. However, poor data quality, an absence of interpretability, and problems with execution may limit their effectiveness. Conversely, AI has the potential to augment clinical judgement, improve patient outcomes, and increase healthcare efficiency. However, concerns about data safety, limited generalisability, and regulatory compliance can make its implementation challenging. It is crucial to comprehend these advantages and limitations to guarantee the efficient implementation and endorsement of these technologies in healthcare. As a whole, healthcare services will be improved because future healthcare providers can use AL and ML to make better decisions about patient evaluation and treatment alternatives

International Journal of Advances in Medicine , 2023

Artificial intelligence (AI) is revolutionizing various medical practices, making them more affordable, efficient, and faster. Its uses range from diagnosis, management, monitoring, and outcome forecasting to individualized care. AI technology in psychotherapy can help conditions such as dementia, autism spectrum disorder, and schizophrenia, and due to its image processing, segmentation, and reconstruction capabilities, AI has found applications in a wide range of fields, including the diagnosis of cancer, the treatment of skin lesions, the prediction of metastasis of malignancies, the staging of lung nodules, the identification of COVID-19, and the classification of thyroid tissue. In addition to histopathology images, imaging techniques such as CT, MRI, mammography, fundus imaging, and even photographs can be used to diagnose patients. In this study, we tried to address the current status and future scope of AI to bring substantial upliftment to health care. It is anticipated that human intelligence and AI will coexist in the field of medicine in the future. Modern smart devices collect a huge amount of data that can be used for disease prevention, health promotion, monitoring, and diagnosis in medicine. AI will improve as long as we train them. With the development of sophisticated machinery, robotics, and virtual reality, the healthcare industry is likely to undergo revolutionary changes. AI has performance on par with that of human experts, with the added benefits of scalability and automation. Before becoming fully autonomous in nature, AI systems might need tight supervision due to their lack of training, limited knowledge, and limited flexibility in clinical settings.

Computational and Mathematical Methods in Medicine, 2021

Artificial intelligence (AI) is making computer systems capable of executing human brain tasks in many fields in all aspects of daily life. The enhancement in information and communications technology (ICT) has indisputably improved the quality of people's lives around the globe. Especially, ICT has led to a very needy and tremendous improvement in the health sector which is commonly known as electronic health (eHealth) and medical health (mHealth). Deep machine learning and AI approaches are commonly presented in many applications using big data, which consists of all relevant data about the medical health and diseases which a model can access at the time of execution or diagnosis of diseases. For example, cardiovascular imaging has now accurate imaging combined with big data from the eHealth record and pathology to better characterize the disease and personalized therapy. In clinical work and imaging, cancer care is getting improved by knowing the tumor biology and helping in ...

Journal of Propulsion Technology, 2023

The field of medicine has been revolutionised by uses of artificial intelligence (AI). Based on a review of the existing literature, this investigation delves deeper into the significance of artificial intelligence in healthcare, examining its impact in six key areas: There are many different kinds of administration software used in the healthcare industry, including (i) imaging and diagnostics, (ii) online patient care, (iii) medical research and drug discovery, (iv) patient engagement and compliance, (v) rehabilitation, and (vi) other applications. The early diagnosis and containment of a coronavirus disease 2019 (COVID-19) outbreak, the provision of virtual patient care utilising AI-powered tools, the management of electronic health records, the improvement of patient engagement and compliance with the treatment plan, and the reduction of health care administrators' administrative workload are just a few examples of how artificial intelligence has made a significant impact on the healthcare industry. However, the scientific method includes AI into medical practise while simultaneously addressing a wide range of difficult logistical, moral, and sociological concerns.