Artificial Intelligence In Healthcare Research Articles

The Integration of AI-Driven Decision Support Systems in Healthcare: Enhancements, Challenges, and Future Directions

Artificial Intelligence (AI) is transforming the healthcare landscape by enhancing diagnostic accuracy, operational efficiency, and patient management through AI-driven Decision Support Systems (AI-DSS). These systems leverage vast datasets, including electronic health records and various biomolecular markers, to provide evidence-based recommendations for clinical practice. Despite their potential, challenges such as data quality, algorithmic bias, and ethical concerns persist. This paper explores the capabilities of AI-DSS in healthcare, the methodologies underpinning their operation, and the ethical considerations that accompany their implementation. Additionally, the paper discusses future trends in AI integration within healthcare, emphasizing the importance of ongoing collaboration between healthcare professionals and AI researchers to address existing limitations and opti-mize patient outcomes. Keywords: Artificial Intelligence, Decision Support Systems, Healthcare, Diagnostic Accuracy, Predictive Analytics, Ethical Considerations, Data Privacy, Machine Learning, Personalized Medicine

Advancing Medical Image Analysis: The Role of Adaptive Optimization Techniques in Enhancing COVID-19 Detection, Lung Infection, and Tumor Segmentation

Artificial intelligence (AI) holds significant potential to revolutionize healthcare by improving clinical practices and patient outcomes. This research explores the integration of AI in healthcare, focusing on methodologies such as machine learning, natural language processing, and computer vision, which enable the extraction of valuable insights from complex medical imaging and clinical data. Through a comprehensive literature review, the study highlights AI’s practical applications in diagnostics, treatment planning, and predicting patient outcomes. Additionally, ethical issues, data privacy, and legal frameworks are examined, emphasizing the importance of responsible AI usage in healthcare. The findings demonstrate AI’s ability to enhance diagnostic accuracy, streamline administrative tasks, and optimize resource allocation, leading to personalized treatments and more efficient healthcare management. However, challenges remain, including data quality, algorithm transparency, and ethical concerns, which must be addressed to ensure safe and effective AI deployment. Continued research, collaboration between healthcare professionals and AI experts, and the development of robust regulatory frameworks are essential for maximizing AI’s benefits while minimizing risks. This research underscores the transformative potential of AI in healthcare and stresses the need for a multidisciplinary approach to address the ethical and regulatory complexities involved in its widespread adoption

Global cross-sectional student survey on AI in medical, dental, and veterinary education and practice at 192 faculties

BackgroundThe successful integration of artificial intelligence (AI) in healthcare depends on the global perspectives of all stakeholders. This study aims to answer the research question: What are the attitudes of medical, dental, and veterinary students towards AI in education and practice, and what are the regional differences in these perceptions?MethodsAn anonymous online survey was developed based on a literature review and expert panel discussions. The survey assessed students' AI knowledge, attitudes towards AI in healthcare, current state of AI education, and preferences for AI teaching. It consisted of 16 multiple-choice items, eight demographic queries, and one free-field comment section. Medical, dental, and veterinary students from various countries were invited to participate via faculty newsletters and courses. The survey measured technological literacy, AI knowledge, current state of AI education, preferences for AI teaching, and attitudes towards AI in healthcare using Likert scales. Data were analyzed using descriptive statistics, Mann–Whitney U-test, Kruskal–Wallis test, and Dunn-Bonferroni post hoc test.ResultsThe survey included 4313 medical, 205 dentistry, and 78 veterinary students from 192 faculties and 48 countries. Most participants were from Europe (51.1%), followed by North/South America (23.3%) and Asia (21.3%). Students reported positive attitudes towards AI in healthcare (median: 4, IQR: 3–4) and a desire for more AI teaching (median: 4, IQR: 4–5). However, they had limited AI knowledge (median: 2, IQR: 2–2), lack of AI courses (76.3%), and felt unprepared to use AI in their careers (median: 2, IQR: 1–3). Subgroup analyses revealed significant differences between the Global North and South (r = 0.025 to 0.185, all P < .001) and across continents (r = 0.301 to 0.531, all P < .001), with generally small effect sizes.ConclusionsThis large-scale international survey highlights medical, dental, and veterinary students' positive perceptions of AI in healthcare, their strong desire for AI education, and the current lack of AI teaching in medical curricula worldwide. The study identifies a need for integrating AI education into medical curricula, considering regional differences in perceptions and educational needs.Trial registrationNot applicable (no clinical trial).

Clinical decision making under uncertainty: a bootstrapped counterfactual inference approach

BackgroundLearning policies for decision-making, such as recommending treatments in clinical settings, is important for enhancing clinical decision-support systems. However, the challenge lies in accurately evaluating and optimizing these policies for maximum efficacy. This paper addresses this gap by focusing on two key aspects of policy learning: evaluation and optimization.MethodWe develop counterfactual policy learning algorithms for practical clinical applications to suggest viable treatment for patients. We first design a bootstrap method for counterfactual assessment and enhancement of policies, aiming to diminish uncertainty in clinical decisions. Building on this, we introduce an innovative adversarial learning algorithm, inspired by bootstrap principles, to further advance policy optimization.ResultsThe efficacy of our algorithms was validated using both semi-synthetic and real-world clinical datasets. Our method outperforms baseline algorithms, reducing the variance in policy evaluation by 30% and the error rate by 25%. In policy optimization, it enhances the reward by 1% to 3%, highlighting the practical value of our approach in clinical decision-making.ConclusionThis study demonstrates the effectiveness of combining bootstrap and adversarial learning techniques in policy learning for clinical decision support. It not only enhances the accuracy and reliability of policy evaluation and optimization but also paves avenues for leveraging advanced counterfactual machine learning in healthcare.

Toward a responsible future: recommendations for AI-enabled clinical decision support.

Integrating artificial intelligence (AI) in healthcare settings has the potential to benefit clinical decision-making. Addressing challenges such as ensuring trustworthiness, mitigating bias, and maintaining safety is paramount. The lack of established methodologies for pre- and post-deployment evaluation of AI tools regarding crucial attributes such as transparency, performance monitoring, and adverse event reporting makes this situation challenging. This paper aims to make practical suggestions for creating methods, rules, and guidelines to ensure that the development, testing, supervision, and use of AI in clinical decision support (CDS) systems are done well and safely for patients. In May 2023, the Division of Clinical Informatics at Beth Israel Deaconess Medical Center and the American Medical Informatics Association co-sponsored a working group on AI in healthcare. In August 2023, there were 4 webinars on AI topics and a 2-day workshop in September 2023 for consensus-building. The event included over 200 industry stakeholders, including clinicians, software developers, academics, ethicists, attorneys, government policy experts, scientists, and patients. The goal was to identify challenges associated with the trusted use of AI-enabled CDS in medical practice. Key issues were identified, and solutions were proposed through qualitative analysis and a 4-month iterative consensus process. Our work culminated in several key recommendations: (1) building safe and trustworthy systems; (2) developing validation, verification, and certification processes for AI-CDS systems; (3) providing a means of safety monitoring and reporting at the national level; and (4) ensuring that appropriate documentation and end-user training are provided. AI-enabled Clinical Decision Support (AI-CDS) systems promise to revolutionize healthcare decision-making, necessitating a comprehensive framework for their development, implementation, and regulation that emphasizes trustworthiness, transparency, and safety. This framework encompasses various aspects including model training, explainability, validation, certification, monitoring, and continuous evaluation, while also addressing challenges such as data privacy, fairness, and the need for regulatory oversight to ensure responsible integration of AI into clinical workflow. Achieving responsible AI-CDS systems requires a collective effort from many healthcare stakeholders. This involves implementing robust safety, monitoring, and transparency measures while fostering innovation. Future steps include testing and piloting proposed trust mechanisms, such as safety reporting protocols, and establishing best practice guidelines.

Navigating artificial intelligence in healthcare: Hurdles and hindrances

Navigating artificial intelligence in healthcare: Hurdles and hindrances

The Use of AI in Enhancing Patient Communication

Artificial Intelligence (AI) has rapidly evolved in the healthcare sector, significantly transforming patient-provider communication. This paper examines the application of AI, particularly through tools like chatbots and virtual assistants, to enhance patient interaction and address communication barriers. It examines how AI-based tools improve patient accessibility, streamline information exchange, and aid in patient care, particularly in countries like Australia. The study also highlights the benefits and challenges associated with AI-driven communication, including efficiency, patient satisfaction, privacy concerns, and ethical considerations. Ultimately, it suggests that AI technology offers promising solutions for improving patient communication but requires further research and careful implementation to overcome challenges. Keywords: AI in healthcare, patient communication, chatbots, virtual assistants, telemedicine, healthcare technology.

Care robot literacy: integrating AI ethics and technological literacy in contemporary healthcare

AbstractHealthcare work is guided by care ethics, and any technological changes, including the use of robots and artificial intelligence (AI), must comply with existing norms, values and work practices. By bridging technological literacy and AI ethics, this study provides a nuanced definition and an integrative conceptualization of care robot literacy (CRL) for contemporary care work. Robotized care tasks require new orientation and qualifications on the part of employees. CRL is considered as one of these new demands, which requires practitioners to have the resources, skills and understanding necessary to work with robots. This study builds on sociotechnical approach of literacy by highlighting a dynamic relationship of care robotization in which successful human–technology interaction relies on exchanges between the technological and the social. Our findings from directed content analysis and theoretical synthesis of in-demand technological literacy and AI ethics in care work emphasize competencies and situational awareness regarding both using the robot and communicating about the care robot. The initial conceptualization of CRL provides a conceptual framework for future studies, implementation and product development of care robots, drastically differing from studying, implementing and developing robots in general. In searching for technologically sound and ethically compliant solutions, the study advocates for the future significance of context-specific CRL as valuable addition to the terminology of ethical AI in healthcare.

Generative AI and large language models in nuclear medicine: current status and future prospects.

This review explores the potential applications of Large Language Models (LLMs) in nuclear medicine, especially nuclear medicine examinations such as PET and SPECT, reviewing recent advancements in both fields. Despite the rapid adoption of LLMs in various medical specialties, their integration into nuclear medicine has not yet been sufficiently explored. We first discuss the latest developments in nuclear medicine, including new radiopharmaceuticals, imaging techniques, and clinical applications. We then analyze how LLMs are being utilized in radiology, particularly in report generation, image interpretation, and medical education. We highlight the potential of LLMs to enhance nuclear medicine practices, such as improving report structuring, assisting in diagnosis, and facilitating research. However, challenges remain, including the need for improved reliability, explainability, and bias reduction in LLMs. The review also addresses the ethical considerations and potential limitations of AI in healthcare. In conclusion, LLMs have significant potential to transform existing frameworks in nuclear medicine, making it a critical area for future research and development.

DrugDoctor: enhancing drug recommendation in cold-start scenario via visit-level representation learning and training.

Medication recommendation is a crucial application of artificial intelligence in healthcare. Current methodologies mostly depend on patient-level longitudinal representation, which utilizes the entirety of historical electronic health records for making predictions. However, they tend to overlook a few key elements: (1) The need to analyze the impact of past medications on previous conditions. (2) Similarity in patient visits is more common than similarity in the complete medical histories of patients. (3) It is difficult to accurately represent patient-level longitudinal data due to the varying numbers of visits. To our knowledge, current models face difficulties in dealing with initial patient visits (i.e. in cold-start scenarios) which are common in clinical practice. This paper introduces DrugDoctor, an innovative drug recommendation model crafted to emulate the decision-making mechanics of human doctors. Unlike previous methods, DrugDoctor explores the visit-level relationship between prescriptions and diseases while considering the impact of past prescriptions on the patient's condition to provide more accurate recommendations. We design a plug-and-play block to effectively capture drug substructure-aware disease information and effectiveness-aware medication information, employing cross-attention and multi-head self-attention mechanisms. Furthermore, DrugDoctor adopts a fundamentally new visit-level training strategy, aligning more closely with the practices of doctors. Extensive experiments conducted on the MIMIC-III and MIMIC-IV datasets demonstrate that DrugDoctor outperforms 10 other state-of-the-art methods in terms of Jaccard, F1-score, and PRAUC. Moreover, DrugDoctor exhibits strong robustness in handling patients with varying numbers of visits and effectively tackles "cold-start" issues in medication combination recommendations.

Making inroads in the Indian AI imaging market

Research methodology This case study involves interviews with radiologists of various hospitals and with company personnel. Both primary and secondary data sources have been used. The first-hand perspective from the radiologists highlighted the challenges they face concerning time and the patient load. The company personnel highlighted using machine learning for used cases to make the platform more robust and accurate. This case has been tested with MBA students. Case overview/synopsis An emerging health-care artificial intelligence (AI) start-up, DeepTek.AI, wants to expand its reach in the radiology market. The company intends to leverage technology to assist radiologists in diagnostics. India's health-care sector faces the challenge of needing more trained doctors and nurses to meet the ever-increasing needs of patients. This case study revolves around the radiologists' concerns about implementing the new technology and its ease of use. The features and benefits of integrating AI in diagnostics are the need of the hour, but the reliability of results needs to be ascertained for adopting it. Complexity academic level This case was written for marketing applications and practices, trends in marketing, marketing strategy and technology adoption in marketing courses at the post-graduate level. Consumer adoption of finance, hospitality, travel and health-care technology is vital for increasing the company's market share and growth prospects. The students will have an opportunity to understand the challenges and the opportunities.

Peer Review of “Artificial Intelligence in Healthcare: 2023 Year in Review (Preprint)”

Peer Review of “Artificial Intelligence in Healthcare: 2023 Year in Review (Preprint)”

Literature Review: The Use of AI in Healthcare

Artificial Intelligence (AI) is transforming healthcare by offering innovative solutions for improving diagnostics, treatment, and patient care. This literature review explores the current state of AI applications in healthcare, including medical imaging, electronic health records (EHRs), drug discovery, and personalized medicine. We examine recent advancements, key benefits, and significant challenges such as data quality, model interpretability, and integration into clinical practice. Additionally, we discuss future research directions to enhance the effectiveness and adoption of AI in healthcare. This review highlights AI's potential to revolutionize healthcare delivery and improve patient outcomes.

Mitigation measures for addressing gender bias in artificial intelligence within healthcare settings: a critical area of sociological inquiry

AbstractArtificial intelligence (AI) is often described as crucial for making healthcare safer and more efficient. However, some studies point in the opposite direction, demonstrating how biases in AI cause inequalities and discrimination. As a result, a growing body of research suggests mitigation measures to avoid gender bias. Typically, mitigation measures address various stakeholders such as the industry, academia, and policy-makers. To the author’s knowledge, these have not undergone sociological analysis. The article fills this gap and explores five examples of mitigation measures designed to counteract gender bias in AI within the healthcare sector. The rapid development of AI in healthcare plays a crucial role globally and must refrain from creating or reinforcing inequality and discrimination. In this effort, mitigation measures to avoid gender bias in AI in healthcare are central tools and, therefore, essential to explore from a social science perspective, including sociology. Sociologists have made valuable contributions to studying inequalities and disparities in AI. However, research has pointed out that more engagement is needed, specifically regarding bias in AI. While acknowledging the importance of these measures, the article suggests that they lack accountable agents for implementation and overlook potential implementation barriers such as resistance, power relations, and knowledge hierarchies. Recognizing the conditions where the mitigation measures are to be implemented is essential for understanding the potential challenges that may arise. Consequently, more studies are needed to explore the practical implementation of mitigation measures from a social science perspective and a systematic review of mitigation measures.

Competing Legal Futures – “Commodification Bets” All the Way From Personal Data to AI

Abstract This Article explores the implications of Artificial Intelligence (AI)-driven innovation across sectors, highlighting the resulting legal uncertainties. Despite the transformative influence of AI in healthcare, retail, finance and more, regulatory responses to these developments are often contradictory, contributing to the opacity of the legal underpinnings of AI business. Our Article notes the common trend of commercializing AI tools amidst legal uncertainty, using innovative contractual solutions to secure claims. Over time, these innovations trigger overlooked legal conflicts, sometimes leading to outright bans on AI products due to negative impacts on some fundamental rights and democratic Governance. The core argument of our Article is that an over-reliance on co-regulatory strategies, such as those proposed by the European AI Act, exacerbates legal instability in emerging technological markets. This panorama creates an ’extended legal present’ when alternative legal expectations coexist, thus causing economic and political uncertainty that may elicit legal instability in the future. The concept of ’competing legal futures’ is introduced to illustrate how economic actors must bet on a legal future in the absence of guarantees that this future will materialize. To help analyze this complex narrative, we propose a theoretical framework for understanding legal, technological, and economic dynamics, highlighting anomalies in market exchanges within the co-regulatory model. Despite the focus on European developments, the practical and theoretical implications extend beyond the EU, making the Article relevant to a broader understanding of the legal-economic challenges posed by AI and digital innovation. We conclude by arguing for a course correction, proposing institutional diversification for resilient governance of legal innovation under uncertainty.

Decoding machine learning in nursing research: A scoping review of effective algorithms.

The rapid evolution of artificial intelligence (AI) technology has revolutionized healthcare, particularly through the integration of AI into health information systems. This transformation has significantly impacted the roles of nurses and nurse practitioners, prompting extensive research to assess the effectiveness of AI-integrated systems. This scoping review focuses on machine learning (ML) used in nursing, specifically investigating ML algorithms, model evaluation methods, areas of focus related to nursing, and the most effective ML algorithms. The scoping review followed the Preferred Reporting Items for Systematic Review and Meta-Analysis Extension for Scoping Reviews (PRISMA-ScR) guidelines. A structured search was performed across seven databases according to PRISMA-ScR: PubMed, EMBASE, CINAHL, Web of Science, OVID, PsycINFO, and ProQuest. The quality of the final reviewed studies was assessed using the Medical Education Research Study Quality Instrument (MERSQI). Twenty-six articles published between 2019 and 2023 met the inclusion and exclusion criteria, and 46% of studies were conducted in the US. The average MERSQI score was 12.2, indicative of moderate- to high-quality studies. The most used ML algorithm was Random Forest. The four second-most used were logistic regression, least absolute shrinkage and selection operator, decision tree, and support vector machine. Most ML models were evaluated by calculating sensitivity (recall)/specificity, accuracy, receiver operating characteristic (ROC), area under the ROC (AUROC), and positive/negative prediction value (precision). Half of the studies focused on nursing staff or students and hospital readmission or emergency department visits. Only 11 articles reported the most effective ML algorithm(s). The scoping review provides insights into the current status of ML research in nursing and recognition of its significance in nursing research, confirming the benefits of ML in healthcare. Recommendations include incorporating experimental designs in research studies to optimize the use of ML models across various nursing domains. The scoping review demonstrates substantial clinical relevance of ML applications for nurses, nurse practitioners, administrators, and researchers. The integration of ML into healthcare systems and its impact on nursing practices have important implications for patient care, resource management, and the evolution of nursing research.

Patient Consent and The Right to Notice and Explanation of AI Systems Used in Health Care

Given the need for enforceable guardrails for artificial intelligence (AI) that protect the public and allow for innovation, the U.S. Government recently issued a Blueprint for an AI Bill of Rights which outlines five principles of safe AI design, use, and implementation. One in particular, the right to notice and explanation, requires accurately informing the public about the use of AI that impacts them in ways that are easy to understand. Yet, in the healthcare setting, it is unclear what goal the right to notice and explanation serves, and the moral importance of patient-level disclosure. We propose three normative functions of this right: (1) to notify patients about their care, (2) to educate patients and promote trust, and (3) to meet standards for informed consent. Additional clarity is needed to guide practices that respect the right to notice and explanation of AI in healthcare while providing meaningful benefits to patients.

AI in Health Care: A Comprehensive Review

AI in Health Care: A Comprehensive Review

Gaining Trust: Lessons and Opportunities for Artificial Intelligence in Health Care.

Gaining Trust: Lessons and Opportunities for Artificial Intelligence in Health Care.

Ethical AI in Healthcare: Balancing Innovation with Privacy and Compliance

Within the dynamic and ever-changing healthcare industry, Artificial Intelligence (AI) emerges as a powerful and revolutionary concept capable of transforming patient care, optimising operational processes, and improving clinical decision-making. Nevertheless, the incorporation of artificial intelligence (AI) into healthcare systems gives rise to substantial ethical considerations, especially pertaining to privacy and adherence to regulations. This work examines the ethical aspects of artificial intelligence (AI) in the healthcare sector, highlighting the importance of maintaining a harmonious equilibrium between technical advancement and strict compliance with privacy regulations and legal obligations. Moreover, the use of artificial intelligence in healthcare must adhere to legislative frameworks specifically created to safeguard the rights of humans. This encompasses not just compliance with current rules but also the proactive identification and resolution of developing legal norms as AI technology advances. Healthcare practitioners and artificial intelligence (AI) developers should cooperate to create explicit standards and procedures that conform to existing legislation and expected future rules.