Top Research Papers on AI in Healthcare

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.

This 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, and spans the entire length of the AI lifecycle: data management, model development, deployment and monitoring.

Recent advances in artificial intelligence (AI) and machine learning have witnessed many successes in various disciplines including the healthcare sector. Innovations in intelligent medical systems have revolutionized the way in which healthcare services are provided, ranging from making clinical diagnosis, developing personalized treatment and drugs, assisting patient monitoring, to automating administrative tasks and reducing operational costs. In this book, the authors present key applications in the general area of health care, where AI has made significant successes.
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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.

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.

This paper categorizes AI applications in healthcare and comprehensively examines the challenges associated with deploying AI in medical practices at scale, highlighting that flawed business models and wrong workflows in healthcare practices cannot be rectified merely by deploying AI-driven tools.

There must be a better solution for a country like Austria in the heart of the European continent that may stem from a Moving Forward thinking community as the authors all represent together today.

AI's present applications in healthcare, including its benefits, limitations and future scope are reviewed, alongside further research into the specific capabilities and limitations of its medical use.

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.

A thorough bibliometric study on healthcare-related artificial intelligence research from the years 2000 to 2021 will help researchers, legislators, and practitioners understand the field's growth and the prerequisites for responsible use of artificial intelligence technology within the healthcare system.

• Emphasizes the need for transparency to build healthcare professionals' trust in AI systems. • Addresses the critical need for explainability due to potential high-impact consequences of AI errors in healthcare. • Categorizes XAI methods into six groups for healthcare research: feature-oriented, global, concept, surrogate, local pixel-based, and human-centric. • Analyzes the significance of XAI in overcoming healthcare-specific challenges. • Provides an exhaustive review of XAI applications and relevant experimental results in healthcare contexts. Explainable Artificial Intelligence (XAI) en...

This work advocates for the creation of hospital units responsible for quality assurance and improvement of these algorithms, which it refers to as “AI-QI” units, and discusses how tools that have long been used in hospitalquality assurance and quality improvement can be adapted to monitor static ML algorithms.

Results indicate that technological (TEC) factors are the most influential factor that impacts the adoption of AI in HSC in the context of emerging economies, followed by institutional or environmental (INT), human (HUM), and organizational (ORG) dimensions.

This report proposes an operational definition of artificial intelligence to be adopted in the context of AI Watch, the Commission knowledge service to monitor the development, uptake and impact of artificial intelligence for Europe. The definition, which will be used as a basis for the AI Watch monitoring activity, is established by means of a flexible scientific methodology that allows regular revision. The operational definition is constituted by a concise taxonomy and a list of keywords that characterise the core domains of the AI research field, and transversal topics such as applications...

AI in structural biology is briefly overviewed, including in molecular dynamics simulations and prediction of microbiota–human protein–protein interactions, and their powerful impact on the life sciences.

An account for determining when an AI system can be said to embody certain values is proposed, which understands embodied values as the result of design activities intended to embed those values in such systems.

There needs to be a concerted effort around not just the creation, but also the delivery of AI, which includes not just machine learning models and their predictions, butAlso the new systems for care delivery that they enable.

Considerations for the use and application of ML in healthcare settings include assessing the quality of data inputs and decision-making that serve as the foundations of the ML model, ensuring the end-product is interpretable, transparent, and ethical concerns are considered throughout the development process.

Recent breakthroughs in the application of AI in healthcare are outlined, a roadmap to building effective, reliable and safe AI systems are described, and the possible future direction of AI augmented healthcare systems are discussed.

This comprehensive review explores key ethical concerns in the domain, including privacy, transparency, trust, responsibility, bias, and data quality, and how to harness the full potential of AI in healthcare while ensuring ethical and equitable outcomes.