Top Research Papers on AI in Healthcare

There’s a famous quote “Machines will not replace physicians but physicians using AI will soon replace those not using it”, but other important healthcare domains of clinical diagnostics or drug discovery are not uninfluenced by use of AI techniques.

CAs, considered value-added AI-driven applications such as CAs or social robots, serve as a coworker to facilitate healthcare practitioners and patients, catering to patients' needs and communication to enhance care delivery experience and improve information flow processes using interactive services within EDs.

The described architecture will provide better and cheaper healthcare and allow researchers access to data that are not available today to further their understanding of the human body and its abnormalities.

The findings suggest that pharmaceutical firms have benefited from AI in healthcare by speeding up their drug discovery process and automating target identification, and the findings indicate that AI-assisted clinical trials are capable of handling massive volumes of data and producing highly accurate results.

Previous studies related to implementation of ethics in AI are analyzed; the literature results indicate that between 2010 and 2021, there were 150 AI ethical incidents; including data privacy and security risks, safety concerns, bias diagnosis, and the possibility of hostile entities taking control of AI.

A summary of the complex and multi-layered Indian healthcare system is provided, followed by the scope of AI applications in Indian healthcare in four broad areas, from administration to drug discovery and management, along with relevant case studies which have shown promising results.

The study recommends the use of Artificial Intelligence in healthcare services in both rural and urban areas to reduce the burden on medical professionals, increase personalized and efficient healthcare service provision especially in times of global pandemic and increase people’s consciousness towards the need for good health through the adoption of positive lifestyle changes.

A low degree of familiarity with AI is revealed, highlighting the need for medical schools and hospitals to establish specialized education and training programs for physicians to improve patient outcomes.

Pioneering the Integration of Artificial Intelligence in Healthcare Yu-Dong Yao'sIAL AI Medicine Pioneering the Integration of Artificial Intelligence in Healthcare is presented.

The modern healthcare landscape involves collection, processing and analysis of large volumes of data, related to medicines, patient records, treatment paradigms, medical reports, immunization, and various other attributes. These data points need to be handled precisely and efficiently, as they lead to critical health decisions. The role of emerging technologies like AI, ML, Deep Learning, IoT and Robotics is gaining importance in handling such data using various algorithms. Each of these algorithms has a large number of applications in the healthcare domain, including decision on appropriate ...

The AI based medical chatbot (Medimate-AI) on which this research topic is based deals with providing medical advice and interact with potential patients visiting the site, assisting them with finding specialists and getting them admittance to the right treatment.

The effects of AI on healthcare is examined to investigate its potential benefits and challenges and to expect even more significant and positive outcomes in the coming years.

In conclusion, AI in healthcare has the potential to improve patient outcomes, but challenges such as funding, security, data privacy, and ethical considerations need to be addressed.

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This chapter describes a pragmatic Commercial Development framework for building digital health tool that overlays Value Model for healthcare IT investments with Patient Activation Measures and innovation management techniques.

The role of machine learning algorithms in healthcare systems is clarified and can enable researchers to usher in a new era of highly efficient genetic therapy.

A 63-year-old female with a history of type 2 diabetes, hypothyroidism, hyperlipidemia, and a family history of cardiovascular disease, who came to the ED with chest pain for three days, palpitations, and dyspnea at rest is presented.

Artificial intelligence's powerful potential to revolutionize healthcare delivery across multiple domains is explored, emphasizing the need to overcome challenges and harness its transformative capabilities in clinical practice.

This study explores machine learning prediction tools and methodologies that are extensively used in a variety of departments and offer alerting and risk management decision-support capabilities for better patient care.

AI shall be employed in Indian healthcare system to address the problems of economic inequality by improving the accessibility of quality medical facilities by improving the efficiency, quality and accessibility of healthcare services to millions of users.

Artificial intelligence when applied in health care resulted in intelligent support to decision-making, optimised business processes, increased quality, monitoring and delivering of personalised treatment plans and many other applications.

The existing and upcoming applications of AI in healthcare are reviewed, with respect to various disciplines, to help clinicians and researchers appraise the current state of the utilization ofAI in healthcare and guide future research.

The arrival of AI and ML will enhance and facilitates the improvement of this relationship through better accuracy, productivity, and workflow, thus develop a health system that will go beyond just curing disease, but further into wellness that preventing disease before it strikes.

Artificial Intelligence (Al) has emerged as a transformative force in various fields, and its potential in medicine is particularly remarkable. The application of Al in healthcare holds immense promise, as it has the ability to revolutionise medical practice and the delivery of healthcare services. Over the past decade, Al has made significant strides in numerous medical specialties, presenting opportunities to enhance patient care, diagnostics, personalised treatment plans, and administrative processes within healthcare organisations. However, despite the tremendous opportunities presented by...

Abstract not available KYAMC Journal Vol. 11, No.-4, January 2021, Page 164-165

The current and future impact of artificial intelligence (AI) technologies on healthcare is discussed and multiple AI systems for the brain, heart, lung, prostate, and musculoskeletal disease are introduced.

This paper gives an outline of the current and not so distant future uses of Artificial Intelligence (AI) in Medicine and Health Care and presents a characterization as per their moral and cultural viewpoints, likely advantages and entanglements, and issues that can be viewed as questionable and are not profoundly talked about in the writing.

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.

Technology's impact on today's world cannot be overstated. Human work is made easier to manage and more productive by technological advancements. Errors attributable to human error have been reduced because of technological advancements in the healthcare sector. Artificial intelligence (AI) has the potential to impact the medical and healthcare industries significantly. The "quadruple goals" of healthcare include better population health, better care for individual patients, better care for individual caregivers, and lower overall healthcare expenses. To achieve these objectives, healthcar...

This work goes over AI applications in stroke in more depth, focusing on the three key areas of early detection and diagnosis, as well as outcome prediction and prognosis analysis.

In these examples, researchers in public health, medicine, and social science as well as engineers will find a path to start interdisciplinary collaborations in health applications of AI/IE/OR.

The present structures are largely fit to deal with the challenge of artificial intelligence are present and it is discussed clearly about the artificial intelligence contribution to the present health care.

A significant Artificial intelligence use case in healthcare is the use of machine learning for medical diagnosis purposes, using patient data and other information, which can provide more accurate diagnoses and treatment plans.

It is demonstrated that artificial intelligence is being used in all domains of life and particularly in the field of healthcare, with a focus on recent advents in the technology of Artificial Intelligence.

Artificial Intelligence is the ever-evolving science and engineering of mimicking human intelligence involving mechanisms that operates more like a human brain, also called Artificial General Intelligence.

The paper addresses the scientific, engineering, and business opportunities for creating effective patient care biomedical systems that are appropriate for next-generation intelligent healthcare using AI-based integrated personalized IoT devices.

The study found that Artificial Intelligence (AI) provides useful insights in Australian healthcare system and is steadily reducing the cost of Australian Healthcare system and improving patients’ overall outcome in Australian Healthcare.

The project investigates the development and evaluation of AI models for medical image analysis, aiming for improved disease detection and classification, and examines the potential of AI-powered clinical decision support systems (CDSS) to assist healthcare professionals.

This work discusses the context that led to the development of a practical training method for a healthcare AI chatbot that efficiently improves chatbot accuracy and patient safety, and developed a postpartum support chatbot named 'Penny'.

The existing utilization of artificial intelligence within the healthcare industry is explored, examining both its scope and potential harms if implemented and relied upon on a broader scale.

The paper investigates how AI-driven predictive analytics have streamlined hospital operations, optimizing resource allocation, staff scheduling, and patient flow, contributing to the overall efficiency of healthcare institutions.

After having a ride in AI vision # 1 (Zorkóczy, 2021a) when an AI investment was to start up a telemedicine project, this article is about asking questions of the opportunities and problems of AI development in the far future.

The investigation found that AI is consistently changing the way healthcare is to be directed in Jordan and can provide solid healthcare services to the stakeholders.

Perception of lay individuals' perceptions about using AI to consult information related to their diagnostic results and what factors influence their perceptions are investigated, with an emphasis on enhancing the trustworthiness of AI and bridging the digital divide.

It is understood that without AI technology rapid development in the healthcare industry could not become possible for them and the adoption of AI technology is important for all health care firms to save their time and efforts for specific work.

The article presents digitalization in the healthcare sector in North America and Europe in order to derive inferences on opportunities to establish a leadership in digitalized, tailored healthcare solutions for individual well-being and public care.

The sanctity of the doctor-patient relationship is deeply embedded in tradition - the Hippocratic oath, medical ethics, professional codes of conduct, and legislation - all of which are being disrupted by big data and 'artificial' intelligence (AI). The transition from paper-based records to electronic health records, wearables, mobile health applications and mobile phone data has created new opportunities to scale up data collection. Databases of unimaginable magnitude can be harnessed to develop algorithms for AI and to refine machine learning. Complex neural networks now lie at the core of ...

This study proposes a comprehensive yet brief review of the challenges and opportunities in healthcare sector by using AI and IoT and provides an overview ofAI and IoT, its applicability, some insights about current trends, outlook on future developments, and challenges of healthcare systems.

A comprehensive analysis and interpretation of different innovation forces that are driving the emergence of a system approach to the infertility sector are presented and the limitations of the use of Machine Learning models in this rapidly developing area are explored.

Medical AI technology could improve physicians’ efficiency and quality of medical services, especially in rural set up, and might be one means of alleviating the inequality between urban and rural health services.