Artificial Intelligence Applications In Healthcare Research Articles

The unmet promise of trustworthy AI in healthcare: why we fail at clinical translation.

Artificial intelligence (AI) has the potential to revolutionize healthcare, for example via decision support systems, computer vision approaches, or AI-based prevention tools. Initial results from AI applications in healthcare show promise but are rarely translated into clinical practice successfully and ethically. This occurs despite an abundance of "Trustworthy AI" guidelines. How can we explain the translational gaps of AI in healthcare? This paper offers a fresh perspective on this problem, showing that failing translation of healthcare AI markedly arises from a lack of an operational definition of "trust" and "trustworthiness". This leads to (a) unintentional misuse concerning what trust (worthiness) is and (b) the risk of intentional abuse by industry stakeholders engaging in ethics washing. By pointing out these issues, we aim to highlight the obstacles that hinder translation of Trustworthy medical AI to practice and prevent it from fulfilling its unmet promises.

Artificial Intelligence, the Digital Surgeon: Unravelling Its Emerging Footprint in Healthcare - The Narrative Review.

Artificial Intelligence (AI) holds transformative potential for the healthcare industry, offering innovative solutions for diagnosis, treatment planning, and improving patient outcomes. As AI continues to be integrated into healthcare systems, it promises advancements across various domains. This review explores the diverse applications of AI in healthcare, along with the challenges and limitations that need to be addressed. The aim is to provide a comprehensive overview of AI's impact on healthcare and to identify areas for further development and focus. The review discusses the broad range of AI applications in healthcare. In medical imaging and diagnostics, AI enhances the accuracy and efficiency of diagnostic processes, aiding in early disease detection. AI-powered clinical decision support systems assist healthcare professionals in patient management and decision-making. Predictive analytics using AI enables the prediction of patient outcomes and identification of potential health risks. AI-driven robotic systems have revolutionized surgical procedures, improving precision and outcomes. Virtual assistants and chatbots enhance patient interaction and support, providing timely information and assistance. In the pharmaceutical industry, AI accelerates drug discovery and development by identifying potential drug candidates and predicting their efficacy. Additionally, AI improves administrative efficiency and operational workflows in healthcare, streamlining processes and reducing costs. AI-powered remote monitoring and telehealth solutions expand access to healthcare, particularly in underserved areas. Despite the significant promise of AI in healthcare, several challenges persist. Ensuring the reliability and consistency of AI-driven outcomes is crucial. Privacy and security concerns must be navigated carefully, particularly in handling sensitive patient data. Ethical considerations, including bias and fairness in AI algorithms, need to be addressed to prevent unintended consequences. Overcoming these challenges is critical for the ethical and successful integration of AI in healthcare. The integration of AI into healthcare is advancing rapidly, offering substantial benefits in improving patient care and operational efficiency. However, addressing the associated challenges is essential to fully realize the transformative potential of AI in healthcare. Future efforts should focus on enhancing the reliability, transparency, and ethical standards of AI technologies to ensure they contribute positively to global health outcomes.

Artificial intelligence in healthcare: Opportunities and challenges

The development of Artificial Intelligence (AI) in healthcare has had a significant impact on healthcare. AI in healthcare can provide more accurate diagnoses and interventions for patients. AI can predict, diagnose, and treat diseases, facilitate the maximum use of healthcare resources by integrating medical information, increase efficiency, and reduce overcrowding of healthcare resources. However, the application of AI in healthcare also faces challenges such as accountability, algorithmic security, and data privacy. This paper discusses the application of AI in healthcare and explores the challenges faced by AI, in-cluding accountability traceability, algorithmic safety, data security, and ethical issues, and makes targeted recommendations. This study provides an in-depth exploration of the application of AI in healthcare, helping to improve the accuracy and efficiency of AI ap-plications in healthcare, as well as providing necessary guidance and references for opti-mizing and enhancing AI technologies.

Algorithmic Precision Medicine: Harnessing Artificial Intelligence for Healthcare Optimization

Artificial Intelligence (AI) has emerged as a transformative force in the healthcare sector, revolutionizing various facets of medical practice, research, and administration. This paper provides a comprehensive exploration of AI applications in healthcare, including diagnostic tools, patient management systems, drug discovery, and ethical considerations. Real-world case studies and success stories illustrate the tangible impact of AI, such as IBM Watson for Oncology and Google DeepMind's Streams, which enhance diagnostic accuracy and early disease detection. Furthermore, emerging research areas in AI, including explainable AI and federated learning, hold promise for addressing ethical and privacy concerns while advancing medical knowledge. The integration of AI into healthcare promises to improve patient outcomes, streamline operations, and reduce costs. This paper underscores the significance of responsible AI deployment and encourages continued research and innovation to harness AI's full potential in shaping the future of healthcare.

Medical Use of Intelligent Systems

This essay delves into the ethical implications of integrating artificial intelligence (AI) into medical practice, addressing a critical gap in the existing research. As AI-driven medical systems have gained prominence over the past decade, there is an urgent need to explore the moral dimensions of their utilization. This study holds significant relevance for academics, healthcare professionals, and policymakers, especially those involved in intelligent medical systems. The exploration begins by dissecting the moral dilemmas surrounding AI applications in healthcare, exploring both technological justifications and concerns. The paper evaluates existing taxonomies developed by researchers in the field, shedding light on the issues that emerge during the development of intelligent medical systems. By scrutinizing the ethical reasons behind healthcare practitioners' adoption of intelligent systems, this research contributes valuable insights for a comprehensive understanding of the subject. This analysis not only informs scholars but also provides crucial guidance to government officials and medical professionals navigating the intricate landscape of AI in medicine.

Managing artificial intelligence applications in healthcare: Promoting information processing among stakeholders

AI applications hold great potential for improving healthcare. However, successfully operating AI is a complex endeavor requiring organizations to establish adequate management approaches. Managing AI applications requires functioning information exchange between a diverse set of stakeholders. Lacking information processing among stakeholders increases task uncertainty, hampering the operation of AI applications. Existing research lacks an understanding of holistic AI management approaches. To shed light on AI management in healthcare, we conducted a multi-perspective literature analysis followed by an interview study. Based on the organizational information processing theory, this paper investigates AI management in healthcare from an organizational perspective. As a result, we develop the AI application management model (AIAMA) that illustrates the managerial factors of AI management in healthcare and its interrelations. Furthermore, we provide managerial practices that improve information processing among stakeholders. We contribute to the academic discourse by providing a conceptual framework that increases the theoretical understanding of AI's management factors and understanding of management interrelations. Moreover, we contribute to practice by providing management practices that promote information processing and decrease task uncertainty when managing AI applications in healthcare.

Securing AI‐based healthcare systems using blockchain technology: A state‐of‐the‐art systematic literature review and future research directions

AbstractHealthcare institutions are progressively integrating artificial intelligence (AI) into their operations. The extraordinary potential of AI is restricted by insufficient medical data for AI model training and adversarial attacks wherein attackers perturb the dataset by adding some noise to it, which leads to the malfunctioning of the AI models, and a lack of trust caused by the opaque operational approach it employs. This Systematic Literature Review (SLR) is a state‐of‐the‐art survey of the research on blockchain technology for securing AI‐integrated healthcare applications. The most relevant articles from the Scopus and Web of Science (WoS) databases were identified using the PRISMA model. Most of the existing literature is about protecting the healthcare data used by AI‐based healthcare systems using blockchain technology, but the modality of data (text, images, audio, and sound) was not specifically mentioned. Information on protecting the training phase and model deployment for AI‐based healthcare systems considering the variations in feature extraction based on the modality of data was also not clearly specified. Hence, the three subfields of AI, namely, natural language processing (NLP), computer vision, and acoustic AI are further studied to identify security loopholes in its implementation pipeline. The three phases, namely the dataset, the training phase, and the trained models need to be protected from adversaries to avoid malfunctioning of the deployed AI models. The nature of the data processed by NLP, computer vision, and acoustic AI, underlying deep neural network (DNN) architectures, the complexity of attacks, and the perceivability of attacks by humans are analyzed to identify the need for security. A blockchain solution for AI‐based healthcare systems is synthesized based on the findings that have demonstrated the distinctive technological features of blockchains. It offers a solution for the privacy and security issues encountered by NLP, computer vision, and acoustic AI to boost the widespread adoption of AI applications in healthcare.

Multidisciplinary Research Mapping in Automation and Artificial Intelligence: A Bibliometric Analysis to Identify Science Convergence

The field of Automation and Artificial Intelligence (AI) has witnessed rapid evolution, marked by interdisciplinary collaborations and groundbreaking advancements. This bibliometric analysis delves into the multidisciplinary research landscape within Automation and AI, aiming to identify science convergence and key trends. Utilizing a comprehensive dataset, we employed co-authorship analysis, citation analysis, keyword analysis, temporal analysis, and VOSviewer visualizations to map the dynamic landscape of Automation and AI research. Our analysis revealed extensive interdisciplinary collaboration among researchers from diverse domains, highlighting the role of cross-disciplinary innovation in advancing the field. Influential authors and highly cited papers were identified, emphasizing the impact of key contributions. Dominant research themes, such as machine learning, ethics in AI, and AI applications in healthcare, emerged from keyword analysis, reflecting the field's evolving priorities. VOSviewer visualizations provided clear representations of science convergence, showcasing the interconnectedness of disciplines like computer science, engineering, ethics, and economics. Interdisciplinary hubs and bridges were identified, underscoring the importance of cross-disciplinary research in shaping the future of Automation and AI. The findings of this analysis offer valuable insights for researchers, policymakers, and practitioners, providing a foundation for enhanced collaboration, ethical considerations, innovation in healthcare, and tailored education and training programs to meet evolving demands.

Centering Public Perceptions on Translating AI Into Clinical Practice: Patient and Public Involvement and Engagement Consultation Focus Group Study.

Artificial intelligence (AI) is widely considered to be the new technical advancement capable of a large-scale modernization of health care. Considering AI's potential impact on the clinician-patient relationship, health care provision, and health care systems more widely, patients and the wider public should be a part of the development, implementation, and embedding of AI applications in health care. Failing to establish patient and public engagement and involvement (PPIE) can limit AI's impact. This study aims to (1) understand patients' and the public's perceived benefits and challenges for AI and (2) clarify how to best conduct PPIE in projects on translating AI into clinical practice, given public perceptions of AI. We conducted this qualitative PPIE focus-group consultation in the United Kingdom. A total of 17 public collaborators representing 7 National Institute of Health and Care Research Applied Research Collaborations across England participated in 1 of 3 web-based semistructured focus group discussions. We explored public collaborators' understandings, experiences, and perceptions of AI applications in health care. Transcripts were coanalyzed iteratively with 2 public coauthors using thematic analysis. We identified 3 primary deductive themes with 7 corresponding inductive subthemes. Primary theme 1, advantages of implementing AI in health care, had 2 subthemes: system improvements and improve quality of patient care and shared decision-making. Primary theme 2, challenges of implementing AI in health care, had 3 subthemes: challenges with security, bias, and access; public misunderstanding of AI; and lack of human touch in care and decision-making. Primary theme 3, recommendations on PPIE for AI in health care, had 2 subthemes: experience, empowerment, and raising awareness; and acknowledging and supporting diversity in PPIE. Patients and the public can bring unique perspectives on the development, implementation, and embedding of AI in health care. Early PPIE is therefore crucial not only to safeguard patients but also to increase the chances of acceptance of AI by the public and the impact AI can make in terms of outcomes.

Potential Impact and Challenges of Implementing Artificial Intelligence in the Romanian Healthcare System

This literature review assesses the prospective implications and advantages of incorporating artificial intelligence (AI) within the healthcare infrastructure of Romania. By exploring the literature on AI applications in healthcare, the article aims to identify opportunities and challenges in adopting AI-driven technologies in the Romanian context. The review findings suggest that AI holds significant promise in improving the diagnosis, prognosis, and treatment processes, while also presenting several ethical and logistical challenges. The article concludes with recommendations for future research and strategies to ensure the responsible implementation of AI technologies in the Romanian healthcare system.

Fairness of artificial intelligence in healthcare: review and recommendations.

In this review, we address the issue of fairness in the clinical integration of artificial intelligence (AI) in the medical field. As the clinical adoption of deep learning algorithms, a subfield of AI, progresses, concerns have arisen regarding the impact of AI biases and discrimination on patient health. This review aims to provide a comprehensive overview of concerns associated with AI fairness; discuss strategies to mitigate AI biases; and emphasize the need for cooperation among physicians, AI researchers, AI developers, policymakers, and patients to ensure equitable AI integration. First, we define and introduce the concept of fairness in AI applications in healthcare and radiology, emphasizing the benefits and challenges of incorporating AI into clinical practice. Next, we delve into concerns regarding fairness in healthcare, addressing the various causes of biases in AI and potential concerns such as misdiagnosis, unequal access to treatment, and ethical considerations. We then outline strategies for addressing fairness, such as the importance of diverse and representative data and algorithm audits. Additionally, we discuss ethical and legal considerations such as data privacy, responsibility, accountability, transparency, and explainability in AI. Finally, we present the Fairness of Artificial Intelligence Recommendations in healthcare (FAIR) statement to offer best practices. Through these efforts, we aim to provide a foundation for discussing the responsible and equitable implementation and deployment of AI in healthcare.

Transforming Healthcare with Artificial Intelligence in Pakistan: A Comprehensive Overview

Artificial Intelligence (AI) is transforming healthcare globally, including in Pakistan. This research journal paper provides an overview of the role and significance of AI in healthcare, ethical considerations, regulatory frameworks, challenges faced byAI healthcare startups, and the current landscape of AI adoption in Pakistan's healthcare system. AI has the potential to revolutionize disease prevention, diagnosis, and treatment by leveraging large healthcare datasets, advanced computational power, and machine learning algorithms. It improves patient outcomes, enhances clinical decision-making, and optimizes healthcare delivery. Globally, AI applications in healthcare encompass medical imaging, clinical decision support, drug discovery, genomics, and remote patient monitoring. AI algorithms accurately diagnose various medical conditions, predict treatment responses, and identify therapeutic targets. Successful AI implementations include combating antimicrobial resistance and improving pediatric healthcare. Ethical considerations in AI healthcare involve bias mitigation, privacy, transparency, and the role of healthcare professionals in shared decision-making. Regulatory frameworks and guidelines are being developed worldwide to ensure safe and responsible AI implementation. Quality criteria for AI-based prediction models focus on performance, interpretability, generalizability, and robustness. Legal and ethical considerations encompass liability, accountability, and the principles of beneficence, autonomy, and justice. In Pakistan, the integration of AI in healthcare can address challenges like limited resources and uneven distribution of healthcare facilities. AI technologies can analyze medical data, diagnose disease outcomes, and personalize treatment plans.

The "PAITA" study: a multicentric cross-sectional study on physicians' attitude and trust towards Artificial Intelligence applications in healthcare

The "PAITA" study: a multicentric cross-sectional study on physicians' attitude and trust towards Artificial Intelligence applications in healthcare

A systematic approach to enhance the explainability of artificial intelligence in healthcare with application to diagnosis of diabetes

Explainable artificial intelligence (XAI) tools are used to enhance the applications of existing artificial intelligence (AI) technologies by explaining their execution processes and results. In most past research, XAI tools and techniques are typically applied to only the inference part of the AI application. This study proposes a systematic approach to enhance the explainability of AI applications in healthcare. Several AI applications for type 2 diabetes diagnosis are taken as examples to illustrate the applicability of the proposed methodology. According to experimental results, the XAI tools and technologies in the proposed methodology were more diverse than those in the past research. In addition, an artificial neural network was approximated to a simpler and more intuitive classification and regression tree (CART) using local interpretable model-agnostic explanation (LIME). The extracted rules were used to recommend actions to the users to restore their health.

A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion

In the last few years, the trend in health care of embracing artificial intelligence (AI) has dramatically changed the medical landscape. Medical centres have adopted AI applications to increase the accuracy of disease diagnosis and mitigate health risks. AI applications have changed rules and policies related to healthcare practice and work ethics. However, building trustworthy and explainable AI (XAI) in healthcare systems is still in its early stages. Specifically, the European Union has stated that AI must be human-centred and trustworthy, whereas in the healthcare sector, low methodological quality and high bias risk have become major concerns. This study endeavours to offer a systematic review of the trustworthiness and explainability of AI applications in healthcare, incorporating the assessment of quality, bias risk, and data fusion to supplement previous studies and provide more accurate and definitive findings. Likewise, 64 recent contributions on the trustworthiness of AI in healthcare from multiple databases (i.e., ScienceDirect, Scopus, Web of Science, and IEEE Xplore) were identified using a rigorous literature search method and selection criteria. The considered papers were categorised into a coherent and systematic classification including seven categories: explainable robotics, prediction, decision support, blockchain, transparency, digital health, and review. In this paper, we have presented a systematic and comprehensive analysis of earlier studies and opened the door to potential future studies by discussing in depth the challenges, motivations, and recommendations. In this study a systematic science mapping analysis in order to reorganise and summarise the results of earlier studies to address the issues of trustworthiness and objectivity was also performed. Moreover, this work has provided decisive evidence for the trustworthiness of AI in health care by presenting eight current state-of-the-art critical analyses regarding those more relevant research gaps. In addition, to the best of our knowledge, this study is the first to investigate the feasibility of utilising trustworthy and XAI applications in healthcare, by incorporating data fusion techniques and connecting various important pieces of information from available healthcare datasets and AI algorithms. The analysis of the revised contributions revealed crucial implications for academics and practitioners, and then potential methodological aspects to enhance the trustworthiness of AI applications in the medical sector were reviewed. Successively, the theoretical concept and current use of 17 XAI methods in health care were addressed. Finally, several objectives and guidelines were provided to policymakers to establish electronic health-care systems focused on achieving relevant features such as legitimacy, morality, and robustness. Several types of information fusion in healthcare were focused on in this study, including data, feature, image, decision, multimodal, hybrid, and temporal.

An improved explainable artificial intelligence tool in healthcare for hospital recommendation

Artificial intelligence (AI) technologies have been widely applied in medicine and healthcare. Explainable AI (XAI) has been proposed to make AI applications more transparent and efficient. This study applies some simple cross-domain tools and techniques, including common expression (with linguistic terms), color management, traceable aggregation, and segmented distance diagrams, among others, to improve the explainability of AI applications in healthcare. Four applications of AI technologies in hospitals were used, and recommendations were studied to illustrate the applicability of the proposed methodology. The explainability of each AI application was evaluated before and after improvement for comparison. According to the experimental results, these AI-based hospital recommendation methods could be better explained by modifying their explanations using simple and cross-domain tools.

Strategies to Improve the Impact of Artificial Intelligence on Health Equity: Scoping Review.

Emerging artificial intelligence (AI) applications have the potential to improve health, but they may also perpetuate or exacerbate inequities. This review aims to provide a comprehensive overview of the health equity issues related to the use of AI applications and identify strategies proposed to address them. We searched PubMed, Web of Science, the IEEE (Institute of Electrical and Electronics Engineers) Xplore Digital Library, ProQuest U.S. Newsstream, Academic Search Complete, the Food and Drug Administration (FDA) website, and ClinicalTrials.gov to identify academic and gray literature related to AI and health equity that were published between 2014 and 2021 and additional literature related to AI and health equity during the COVID-19 pandemic from 2020 and 2021. Literature was eligible for inclusion in our review if it identified at least one equity issue and a corresponding strategy to address it. To organize and synthesize equity issues, we adopted a 4-step AI application framework: Background Context, Data Characteristics, Model Design, and Deployment. We then created a many-to-many mapping of the links between issues and strategies. In 660 documents, we identified 18 equity issues and 15 strategies to address them. Equity issues related to Data Characteristics and Model Design were the most common. The most common strategies recommended to improve equity were improving the quantity and quality of data, evaluating the disparities introduced by an application, increasing model reporting and transparency, involving the broader community in AI application development, and improving governance. Stakeholders should review our many-to-many mapping of equity issues and strategies when planning, developing, and implementing AI applications in health care so that they can make appropriate plans to ensure equity for populations affected by their products. AI application developers should consider adopting equity-focused checklists, and regulators such as the FDA should consider requiring them. Given that our review was limited to documents published online, developers may have unpublished knowledge of additional issues and strategies that we were unable to identify.

Medical errors and patient safety: Strategies for reducing errors using artificial intelligence

Background: Medical errors remain a significant challenge in healthcare, contributing to adverse patient outcomes, increased costs, and extended hospitalizations. These errors encompass diagnostic inaccuracies, medication mistakes, surgical errors, and communication breakdowns. The global prevalence of medical errors underscores the urgent need for effective strategies to enhance patient safety. Aim: This article explores the role of Artificial Intelligence (AI) in reducing medical errors and improving patient safety. It aims to evaluate how AI technologies can mitigate various types of medical errors, and the challenges associated with their implementation. Methods: The study reviews current literature on AI applications in healthcare, focusing on diagnostic support, medication safety, surgical precision, and patient monitoring. It analyzes the effectiveness of AI-driven systems in reducing errors across different medical disciplines and examines the integration challenges, including ethical and regulatory concerns. Results: AI technologies, including machine learning algorithms and decision support systems, have demonstrated significant potential in enhancing diagnostic accuracy, preventing medication errors, and improving surgical outcomes. AI-driven systems have shown promising results in real-time patient monitoring, early detection of adverse events, and optimizing healthcare management. However, challenges related to data privacy, algorithm transparency, and integration into clinical workflows persist.

Are physicians and medical students ready for artificial intelligence applications in healthcare?

Artificial intelligence (AI) Healthcare applications are listed in the national visions of some Gulf Cooperation Council countries. A successful use of AI depends on the attitude and perception of medical experts of its applications. To evaluate physicians and medical students' attitude and perception on AI applications in healthcare. A web-based survey was disseminated by email to physicians and medical students. A total of 293 (82 physicians and 211 medical students) individuals have participated (response rate is 27%). Seven participants (9%) reported knowing nothing about AI, while 208 (69%) were aware that it is an emerging field and would like to learn about it. Concerns about AI impact on physicians' employability were not prominent. Instead, the majority (n=159) agreed that new positions will be created and the job market for those who embrace AI will increase. They reported willingness to adapt AI in practice if it was incorporated in international guidelines (30.5%), published in respected scientific journals (17.1%), or included in formal training (12.2%). Almost two of the three participants agreed that dedicated courses will help them to implement AI. The most commonly reported problem of AI is its inability to provide opinions in unexpected scenarios. Half of the participants think that both the manufacturer and physicians should be legally liable for medical errors occur due to AI-based decision support tools while more than one-third (36.77%) think that physicians who make the final decision should be legally liable. Senior physicians were found to be less familiar with AI and more concerned about physicians' legal liability in case of a medical error. Physicians and medical students showed positive attitudes and willingness to learn about AI applications in healthcare. Introducing AI learning objectives or short courses in medical curriculum would help to equip physicians with the needed skills for AI-augmented healthcare system.

Ethical considerations in implementing AI for mortality prediction in the emergency department: Linking theory and practice.

Artificial intelligence (AI) is predicted to be a solution for improving healthcare, increasing efficiency, and saving time and recourses. A lack of ethical principles for the use of AI in practice has been highlighted by several stakeholders due to the recent attention given to it. Research has shown an urgent need for more knowledge regarding the ethical implications of AI applications in healthcare. However, fundamental ethical principles may not be sufficient to describe ethical concerns associated with implementing AI applications. The aim of this study is twofold, (1) to use the implementation of AI applications to predict patient mortality in emergency departments as a setting to explore healthcare professionals' perspectives on ethical issues in relation to ethical principles and (2) to develop a model to guide ethical considerations in AI implementation in healthcare based on ethical theory. Semi-structured interviews were conducted with 18 participants. The abductive approach used to analyze the empirical data consisted of four steps alternating between inductive and deductive analyses. Our findings provide an ethical model demonstrating the need to address six ethical principles (autonomy, beneficence, non-maleficence, justice, explicability, and professional governance) in relation to ethical theories defined as virtue, deontology, and consequentialism when AI applications are to be implemented in clinical practice. Ethical aspects of AI applications are broader than the prima facie principles of medical ethics and the principle of explicability. Ethical aspects thus need to be viewed from a broader perspective to cover different situations that healthcare professionals, in general, and physicians, in particular, may face when using AI applications in clinical practice.