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.

The idea is to create a medical chatbot using Artificial Intelligence that can diagnose the disease and provide basic details about the disease before consulting a doctor to reduce healthcare costs and improve accessibility to medical knowledge through medical chatbot.

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 design etc.

Artificial intelligence (AI) in healthcare is becoming increasingly important, given its potential to generate and analyse healthcare data to improve patient care and reduce costs and clinical risk while enhancing administrative processes within organisations. AI can introduce new sources of growth, change how people work and improve the effectiveness of their work. Consequently, implementing AI systems in healthcare can enable the optimisation of healthcare resources, facilitate a better patient experience, improve population health, reduce per capita costs, and improve the satisfaction of he...

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.

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 to ensure that medical AI lives up to its promises.

Fairness in machine learning is outlined through the lens of healthcare, and how algorithmic biases arising in clinical workflows can cause healthcare disparities are discussed.

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.

This review provides a comprehensive examination of the integration of Artificial Intelligence (AI) into healthcare, focusing on its transformative implications and challenges. Utilising a systematic search strategy across electronic databases such as PubMed, Scopus, Embase, and ScienceDirect, relevant peer-reviewed articles published in English between January 2010 till date were identified. Findings reveal AI's significant impact on healthcare delivery, including its role in enhancing diagnostic precision, enabling treatment personalisation, facilitating predictive analytics, automating task...

The impact of AI technologies on elderly healthcare is promising and that AI technologies are capable of satisfying the unmet care needs of older adults and demonstrating great potential in its further development in this area.

The principle behind artificial intelligence is mimicking human intelligence in the way that it can perform tasks, recognize patterns, or predict outcomes through learning from the acquired data of various sources. Artificial intelligence and machine learning algorithms have been widely used in autonomous driving, recommender systems in electronic commerce and social media, fintech, natural language understanding, and question answering systems. Artificial intelligence is also gradually changing the landscape of healthcare research (Yu et al. in Biomed Eng 2:719-731, 25). The rule-based approa...

Artificial intelligence has the potential to assist in the diagnosis of skin lesions and may have particular value at the interface between primary and secondary care.

Artificial Intelligence (AI) emerged as an intervention for data and number-related problems. This breakthrough has led to several technological advancements in virtually all fields from engineering to architecture, education, accounting, business, health, and so on. AI has come a long way in healthcare, having played significant roles in data and information storage and management – such as patient medical histories, medicine stocks, sale records, and so on; automated machines; software and computer applications like diagnostic tools such as MRI radiation technology, CT diagnosis and many mor...

Due to popular successes (e.g., ChatGPT) Artificial Intelligence (AI) is on everyone's lips today. When advances in biotechnology are combined with advances in AI unprecedented new potential solutions become available. This can help with many global problems and contribute to important Sustainability Development Goals. Current examples include Food Security, Health and Well-being, Clean Water, Clean Energy, Responsible Consumption and Production, Climate Action, Life below Water, or protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat deser...

This special edition takes an important step in illuminating both what the authors know and what they yet need to research on AI, and raises the issue of whether marketing professionals will embrace AI as a means to free them from more mundane tasks to spend time on higher value activities, or will they viewAI as a threat to their employment.

A mixed-method study identifies the constituents of responsible AI in the healthcare sector and investigates its role in value formation and market performance in India, where AI technologies are in the developing phase.

Artificial Intelligence (AI) has attracted extant literature devoted to different subjects, including healthcare. AI studies within healthcare, however, have focused extensively on medical diagnosis, operations, and prescription, to the neglect of supply chain management (SCM). To bridge this research gap, we draw on corporate social responsibility (CSR) as a theoretical lens to explore how an AI-enhanced medical drone application in Ghana’s healthcare supply chain (HSC) improves the HSC system and contributes to sustainable development. The data for this study is collated through documentary ...

The literature indicates that XAI in healthcare is still a novel subject that has to be explored more in the future, and the main goal of this article was to discover a point of intersection between the two fields.

A responsible AI initiative framework is proposed that encompasses five core themes for AI solution developers, healthcare professionals, and policy makers, summarized in the acronym SHIFT: Sustainability, Human centeredness, Inclusiveness, Fairness, and Transparency.

Abstract Introduction Healthcare systems are complex and challenging for all stakeholders, but artificial intelligence (AI) has transformed various fields, including healthcare, with the potential to improve patient care and quality of life. Rapid AI advancements can revolutionize healthcare by integrating it into clinical practice. Reporting AI’s role in clinical practice is crucial for successful implementation by equipping healthcare providers with essential knowledge and tools. Research Significance This review article provides a comprehensive and up-to-date overview of the current state o...

This manuscript endeavors to unravel the intricacies of recent AI advancements and their profound implications for reconceptualizing the delivery of medical care, advocating for an expansion of research efforts aimed at navigating the ethical complexities inherent to a technology-evolving landscape, catalyzing policy innovation, and devising AI applications that are not only clinically effective but also earn the trust of the patient populace.

The implications of the AI Act for the healthcare sector are highlighted and it is concluded that, due to its horizontal approach, it is necessary to adopt further guidelines to address the unique needs of the healthcare sector.

This study summarizes the state-of-the-art approaches for preserving privacy in AI-based healthcare applications and identifies prominent privacy-preserving techniques such as Federated Learning and Hybrid Techniques along with potential privacy attacks, security challenges, and future directions.