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.

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.

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.

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.

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.

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 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 ...

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

Artificial intelligence has been put forth as a technological innovation which can change the way in which healthcare will be delivered in the near future, but whether the aim of the AI developers and the society it serves are aligned is difficult to ascertain.

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 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.

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.

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.

The study concludes that artificial intelligence has the potential to revolutionize the healthcare industry, enabling more accurate diagnoses, personalized treatments, and improved patient outcomes.

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.

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.

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.

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 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.

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It is claimed that the regulation of AI in healthcare is going to decide the fate of AI-ethics in healthcare for at least the coming decade and the ethical agoras on the regulation of AI-healthcare should instead stamp and remain as the imprint on possible and permissible moral and ethical philosophy of AI in healthcare shaped over the years.

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.

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.

AI can create a paradigm shift in healthcare by complementing and enhancing the skills of healthcare providers, ultimately leading to improved service quality, patient outcomes, and a more efficient healthcare system.

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.

The major categories of issues that AI systems are ideally equipped to resolve followed by clinical diagnostic tasks are enlisted and the difficulties, constraints and biases that must be meticulously addressed for the effective delivery of AI in the health-care sector are discussed.

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.

This study explores the implications of AI on healthcare management, and challenges involved with using AI in healthcare along with the review of several research papers that used AI models in different sectors of healthcare like Dermatology, Radiology, drug Interactions, and Discovery etc.

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 review points out the necessity for seamless integration, interoperability, and the maintenance of AI systems' reliability and accuracy to fully leverage AI's potential in advancing healthcare.

The position and importance of AI in improving the functionality, detection accuracy, decision-making ability of IoMT devices, and evaluation of associated risks assessment is discussed carefully and critically in this review.

In an era where technological advancements are rapidly transforming industries, healthcare is the primary beneficiary of such progress [...]

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.

The governance of AI applications is crucial for patient safety and accountability and for raising HCPs’ belief in enhancing acceptance and boosting significant health consequences, and effective governance is a prerequisite to precisely address regulatory, ethical, and trust issues while advancing the acceptance and implementation of AI.

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.

This study investigated the combination of Emotional Intelligence and AI adoption in the healthcare system in Jordan, guided by the Unified Theory of Acceptance and Use of Technology (UTAUT) model and suggested that the UTAUT model should be further expanded to encompass emotional intelligence as its fifth construct.

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 ...

Public documentation of authorized medical AI products in Europe lacks sufficient public transparency to inform about safety and risks, and is called on lawmakers and regulators to establish legally mandated requirements for public and substantive transparency to fulfill the promise of trustworthy AI for health.

This paper focuses on the Medical, hospital related uses of IT, such as better clinical decision-making, imaging and history taking, reduced duplication of diagnostics, better reliability of medication, increased utilization of health facilities and preventive health initiatives.

The goal of this study is to keep track of current advancements in science, understand technological availability, recognize the enormous power of AI in healthcare, and encourage scientists to use AI in their related fields of research.

By examining the intersection of technology and human well-being, this manuscript provides a holistic view of how AI is revolutionizing healthcare and offers valuable insights into the future of medicine.

A global regulatory convergence for AI in healthcare, similar to the voluntary AI code of conduct that is being developed by the US-EU Trade and Technology Council, would be beneficial to all nations, be it developing or developed.

The purpose of this review is to ascertain the ethical concerns of AI applications in healthcare, to identify the knowledge gaps and provide recommendations for an ethical and legal framework.

Deep learning and neural networks are the most used AI technologies; Bayesian nonparametric models are the potential technologies for clinical trial design; natural language processing and wearable devices are used in patient identification and clinical trial monitoring.

Various other technologies are being developed to facilitate the performance of health workers to lighten their workload, such as robots to help paralyzed patients, automatic operating robots, and other technologies that can help ease the burden on health workers' performance.

This review epitomizes the rearmost of operations of AI in biomedicine, including complaint diagnostics, living backing, biomedical information processing, and biomedical exploration, and epitomize the profitable, legal and counteraccusations ofAI in healthcare.

A review article examines the applications of AI and robotics in elderly care, focusing on their role in promoting independence, monitoring health, helping, and enhancing social interaction.

The FUTURE-AI guideline is described as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare and is described as a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice.

To operationalise trustworthy AI in healthcare, a set of 30 best practices were defined, addressing technical, clinical, socioethical, and legal dimensions, and cover the entire lifecycle of healthcare AI, from design, development, and validation to regulation, deployment, and monitoring.