Artificial Intelligence In Healthcare Research Articles

BwMMV-pred: a novel ensemble learning approach using blood smear images for malaria prediction

The use of machine learning in healthcare has become widespread, enhancing the capabilities of doctors and clinicians. This study introduces a novel ensemble learning approach named Blending with Meta Majority Voting (BwMMV) for malaria prediction using blood smear images. The BwMMV technique combines the strengths of eight base classifiers to form an intermediate dataset, which is subsequently used to train five distinct meta-models using different machine learning algorithms. A Local Binary Pattern Histogram (LBPH) method is employed to extract texture features from blood smear images, effectively capturing the underlying patterns necessary for classification. The final classification decision is determined through a majority voting mechanism, selecting the outcome with the most votes as the final prediction. Our results indicate that the BwMMV approach significantly outperforms traditional hard voting and blending techniques, achieving superior accuracy, robustness, and resilience in performance. This innovative method demonstrates promising potential as a powerful tool for automated diagnosis systems, with the ability to be expanded to analyze various datasets efficiently.

Analyzing the Impact of Scientific and Technological Innovations on Financial Performance: A PCA-Based Study of an AI Healthcare Startup

This study explores the relationship between scientific and technological innovations and the financial performance of startups, focusing on a representative Artificial Intelligence (AI) healthcare startup. Utilizing Principal Component Analysis (PCA), the research aims to dissect the complex interplay between innovation-related metrics—such as R&D spending, patent counts, and technology adoption rates—and financial outcomes like revenue growth, profitability, and market share. The PCA methodology enabled the reduction of high-dimensional data into PCA, which clearly illustrates how several dimensions of innovation impact financial metrics. The approach used in the investigation helps people comprehend more thoroughly how development benefits business viability in several different manners and provides startups with practical advice to use when preparing their revolutionary investments. This paper aims to assist the ecosystem of startup consumers (including investors, business owners, and policymakers) in making better decisions that balance technological progress with economic objectives.

AI in Surgery: Navigating Trends and Managerial Implications Through Bibliometric and Text Mining Odyssey.

Background:This research employs bibliometric and text-mining analysis to explore artificial intelligence (AI) advancements within surgical procedures. The growing significance of AI in healthcare underscores the need for healthcare managers to prioritize investments in this technology. Purpose:To assess the increasing impact of AI on surgical practices through a comprehensive analysis of scientific literature, providing insights that can guide managerial decision-making in adopting AI solutions.Research Design:The study analyzes over 6000 scientific articles published since 1990 to evaluate trends and contributions in the field, informing managers about the current landscape of AI in surgery.Study Sample:The research focuses on publications from various influential publishers across North America, Northern Asia, and Eastern & Western Europe, highlighting key markets for AI implementation in surgical settings.Data Collection and Analysis:A bibliometric approach was utilized to identify key contributors and influential journals. At the same time, text-mining techniques highlighted significant keywords related to AI in surgery, aiding managers in recognizing essential areas for further exploration and investment.Results:The year 2022 marked a significant upsurge in publications, indicating widespread AI integration in healthcare. The U.S. emerged as the foremost contributor, followed by China, the UK, Germany, Italy, the Netherlands, and India. Key journals, such as Annals of Surgery and Spine Journal, play a crucial role in disseminating research findings, serving as valuable resources for managers seeking to stay informed.Conclusions:The findings underscore AI's pivotal role in enhancing diagnostic precision, predicting treatment outcomes, and improving operational efficiency in surgical practices. This progress represents a significant milestone in modern medical science, paving the way for intelligent healthcare solutions and further advancements in the field. Healthcare managers should leverage these insights to foster innovation and improve patient care standards.

Postoperative Otoplasty Care With ChatGPT-4: A Study on Artificial Intelligence (AI)-Assisted Patient Concern and Education.

Otoplasty is a cosmetic surgery that is performed to alter the size, shape, or position of the ear by using permanent stitches. Its main purpose is to correct protruding ears, a condition known as prominauris. After the surgery, it is crucial to provide proper care to ensure successful recovery. However, obtaining timely medical advice can be difficult, especially in remote areas or places with limited resources. To address this issue, incorporating advanced artificial intelligence (AI) tools like Chat Generative Pre-trained Transformer (ChatGPT)-4 into postsurgical care could help fill the gap in patient education and support. This study aims to assess whether ChatGPT4 can be a reliable, accurate, and effective method for answering the most common patient questions and concerns post-otoplasty. The main objective was to assess the AI chatbot's capacity to deliver precise, concise, and pertinent information, especially in situations where health care professionals are limited in availability. In this study, over 50 patients were engaged, and ChatGPT4 was employed to present the same 5 common postoperative questions post-otoplasty surgery care. The AI chatbot's responses were analyzed for accuracy, response time, clarity, and relevance. The chatbot could potentially provide timely assistance, answer questions, and address concerns related to postsurgical care in otoplasty. The responses exhibited a perfect accuracy rate of 100%, closely corresponding to existing medical guidelines. This study explores the potential of AI-driven solutions to enhance patient education and support, especially in areas where access to health care professionals may be limited. However, professional medical advice is crucial in postoperative care and cannot be replaced by ChatGPT-4. By leveraging AI tools like chatbots, individuals in remote or resource-limited settings can potentially receive valuable information and guidance, contributing to successful rehabilitation and overall health care outcomes. Ethical considerations around the use of AI in health care must also be carefully addressed to ensure patient privacy, data security, and appropriate clinical oversight.

The Role of Artificial Intelligence in Healthcare: A Critical Analysis of Its Implications for Patient Care

Artificial Intelligence (AI) is rapidly transforming healthcare, with significant implications for patient care. This article critically analyzes AI's role in improving healthcare delivery, focusing on diagnostic accuracy, personalized treatments, and system efficiency. It highlights key benefits such as enhanced decision-making, reduced human error, and the potential for better patient outcomes through AI-driven tools like predictive analytics and robotic surgery. However, the article also addresses challenges including ethical concerns, algorithmic bias, data privacy issues, and the need for clear regulations and accountability structures. The study explores how AI affects healthcare professionals, reshaping their roles and requiring new skill sets. Through case studies, the article illustrates both the successes and limitations of AI in clinical applications. Ultimately, this critical analysis emphasizes that while AI holds promise in improving patient care, responsible implementation is necessary to address ethical, legal, and technical challenges.

Thinking machines: artificial intelligence in rehabilitation and beyond

In this editorial, Massimiliano Polastri discusses the potential of artificial intelligence in healthcare.

Artificial Intelligence And Its Implications In Health Care: A Textual Analysis

This all-inclusive research paper plunges into the transformative nature of artificial intelligence (AI) in healthcare, emphasizing its ability to change patient care and administrative activities. The investigation examines systematically how AI technologies are creating major improvements towards patients’ outcomes and enhanced operational efficiency within the healthcare systems. From diagnostics to personalized treatment plans and administrative tasks, AI is being integrated using sophisticated algorithms in numerous aspects of healthcare delivery. One major focus is improving diagnostic accuracy. AI-backed diagnostic tools such as image recognition software as well as predictive models have outperformed traditional methods in medical condition identification and diagnosis. For instance, artificial intelligence algorithms show exceptional skills in analysing medical images resulting to earlier detection as well as accurate diagnoses of diseases like cancer, cardiovascular conditions among others neurological disorders. These enhancements not only allow for timely intervention but they significantly reduce instances where diagnosis goes wrong hence enhancing patient prognosis while reducing health costs drastically. The ability to customize personalized therapy is yet another essential aspect of AI in healthcare. With such information as genetic data, medical records and lifestyle factors, and other patient information, AI systems can develop individualized treatment plans. It is for this reason that personalization of treatments enhances their efficiency while reducing the risk of any adverse effects as well as ensuring patients’ adherence to prescribed therapies. Besides, it is important to note that AI-driven predictive models are helpful in identifying individuals with a higher probability of developing specific conditions thereby allowing interventions aimed at preventing diseases and bettering overall population health. AI has also transformed administrative tasks within hospitals besides its clinical applications. For example, AI driven automation is now used to streamline scheduling of appointments or billing processes or even storage of patient records which are normally considered routine administrative works. In addition, through these means there is reduced burden on administrative duties for healthcare workers who continue to perform them with increased precision and timeliness. The net effect is that time and resources can be redirected towards more direct patient care thus improving service quality across the board’s entire system

Toward real-world deployment of machine learning for health care: External validation, continual monitoring, and randomized clinical trials.

In this commentary, we elucidate three indispensable evaluation steps toward the real-world deployment of machine learning within the healthcare sector and demonstrate referable examples for diagnostic, therapeutic, and prognostic tasks. We encourage researchers to move beyond retrospective and within-sample validation, and step into the practical implementation at the bedside rather than leaving developed machine learning models in the dust of archived literature.

Opportunities to Apply Human-centered Design in Health Care With Artificial Intelligence-based Screening for Diabetic Retinopathy.

Diabetic retinopathy (DR) is a leading cause of blindness. Artificial intelligence (AI) has been proposed to provide a novel opportunity to increase screening for DR. While it is paramount to ensure AI has adequate technical capabilities to perform accurate screening, it is also important to assess how to best implement such technology into clinical practice. Human-centered design offers a methodology to understand the real-world context and behaviors of individuals, engage stakeholders, and rapidly prototype and test solutions, enhancing usability and avoiding unintended consequences. This review describes the methodology of human-centered design, examining how it has been used within a variety of health care contexts, with a particular focus on how it has been used to implement an AI-based DR screening program. Further research is needed to understand the best strategies to implement and evaluate AI in health care.

Is My Doctor Human? Acceptance of AI among Patients with Breast Cancer.

Artificial intelligence (AI) is becoming increasingly important in society, and medicine can benefit from its advantages. What scenario can we envision when AI becomes as powerful and accurate as human physicians? How will the traditional patient-doctor relationship be affected by AI? Will patients come to trust and accept AI-assisted healthcare as much as their human counterparts? Our research team has been working on applications of AI in plastic surgery for more than 4 years. Between 2020 and 2024, AI algorithms were developed by the authors and applied on patients for symmetry evaluation after breast cancer surgery. Patients were aware of being evaluated with images for AI model training and assessment. Feedback was reported, and a survey was carried out among patients who underwent evaluation by our team. Among patients with breast cancer who underwent surgical reconstruction, 65% of patients reported very high levels of comfort with AI, given that it was mediated by a human doctor. Patients stated that nondoctor-mediated AI in medicine would greatly reduce trust. The influence of AI on the patient-doctor relationship is an important aspect that will greatly affect medicine. In this preliminary work, patients showed high levels of trust and comfort with the use of AI in healthcare, despite stating that they knew little about AI. Patients insisted that the mediation of a human doctor is key for acceptance. Currently, little is known about the acceptance of AI in medical roles among patients.

Artificial intelligence (AI) applications in healthcare and considerations for nursing education

Aim/objectiveTo review the current AI applications in healthcare and explore the implications for nurse educators in innovative integration of this technology in nursing education and training programs. BackgroundThere are a variety of Artificial Intelligence (AI) applications currently supporting patient care in many healthcare settings. A nursing workforce that leverages healthcare technology to enhance efficiency and accuracy of patient health outcomes is necessary. Nurse educators must understand the various uses of AI applications in healthcare to equip themselves to effectively prepare students to use the applications. DesignQualitative synthesis and analysis of existing literature. MethodsGenerative AI (ChatGPT) was used to support the development of a list of the current AI applications in healthcare. Each application was evaluated for relevance and accuracy. A literature review to define and understand the use of each application in clinical practice was completed. The search terms “AI” and “Health Education” were used to review the literature for evidence on educational programs used for training learners. ResultsTen current applications of AI in healthcare were identified and explored. There is little evidence that outlines how to integrate AI education into educational training for nurses. ConclusionA comprehensive multimodal educational approach that uses innovative learning strategies has potential to support the integration of AI concepts into nursing curriculum. The use of simulation and clinical practicum experiences to support experiential learning and to offer opportunities for practical application and training. Considerations for ethical use and appropriate critical evaluation of AI applications are necessary.

Equipping Future Physicians with Artificial Intelligence Competencies through Student Associations

Advances in artificial intelligence (AI) in the medical sector necessitate the development of AI literacy among future physicians. This article explores the pioneering efforts of the AI in Medicine Association (AIM) at Brigham Young University, which offers a framework for undergraduate pre-medical students to gain hands-on experience, receive principled education, explore ethical considerations, and learn appraisal of AI models. By supplementing formal, university-organized pre-medical education with a student-led, faculty-supported introduction to AI through an extracurricular academic association, AIM alleviates apprehensions regarding AI in medicine early and empowers students preparing for medical school to navigate the evolving landscape of AI in healthcare responsibly.

S2152 Understanding of Artificial Intelligence in Healthcare Among Gastroenterology Fellows and Early Career Gastroenterologists

S2152 Understanding of Artificial Intelligence in Healthcare Among Gastroenterology Fellows and Early Career Gastroenterologists

A Glossary of Terms in Artificial Intelligence for Healthcare

In recent decades, artificial intelligence (AI) has infiltrated a variety of domains, including media, education, and medicine. There exists no glossary, lexicon, or reference for the uninitiated medical professional to explore the new terminology. As AI-driven technologies and applications become more available for clinical use in healthcare settings, an understanding of basic components, models, and tasks related to AI is crucial for clinical and academic appraisal. Here, we present a glossary of AI definitions that healthcare professionals can utilize to augment personal understanding of AI during this fourth industrial revolution.

S1099 Social Media and AI in Healthcare: Understanding Patient Perspectives on Chatbots for Managing IBD

S1099 Social Media and AI in Healthcare: Understanding Patient Perspectives on Chatbots for Managing IBD

Reducing bias in healthcare artificial intelligence: A white paper.

Objective: Mitigation of racism in artificial intelligence (AI) is needed to improve health outcomes, yet no consensus exists on how this might be achieved. Methods: At an international conference in 2022, experts gathered to discuss strategies for reducing bias in healthcare AI. Results: This paper delineates these strategies along with their corresponding strengths and weaknesses and reviews the existing literature on these strategies. Conclusions: Five major themes resulted: reducing dataset bias, accurate modeling of existing data, transparency of artificial intelligence, regulation of artificial intelligence and the people who develop it, and bringing stakeholders to the table.

Leveraging Generative AI for Personalized Healthcare: Revolutionizing Treatment Plans

This comprehensive article explores the transformative potential of generative artificial intelligence (AI) in healthcare, examining its applications in data analysis, disease outcome prediction, and personalized treatment planning. The article discusses how generative AI systems can process vast amounts of multi-modal patient data to uncover subtle patterns and correlations, leading to more accurate diagnoses and tailored treatment strategies. It highlights successful implementations across various medical specialties, including oncology, chronic disease management, and rare disease diagnosis. The article also addresses the challenges and ethical considerations associated with the deployment of generative AI in healthcare, emphasizing the need for data privacy, algorithmic transparency, and regulatory compliance. By presenting both the opportunities and obstacles, this work provides a balanced view of the role generative AI can play in ushering in a new era of precision medicine and patient-centered care

Explainable AI (XAI) in healthcare: Enhancing trust and transparency in critical decision-making

The integration of artificial intelligence (AI) in healthcare is revolutionizing diagnostic and treatment procedures, offering unprecedented accuracy and efficiency. However, the opacity of many advanced AI models, often described as "black boxes," creates challenges in adoption due to concerns around trust, transparency, and interpretability, particularly in high-stakes environments like healthcare. Explainable AI (XAI) addresses these concerns by providing a framework that not only achieves high performance but also offers insight into how decisions are made. This research explores the application of XAI techniques in healthcare, focusing on critical areas such as disease diagnostics, predictive analytics, and personalized treatment recommendations. The study will analyse various XAI methods, including model-agnostic approaches (LIME, SHAP), interpretable deep learning models, and domain-specific applications of XAI. It also evaluates the ethical implications, such as accountability and bias mitigation, and how XAI can foster collaboration between clinicians and AI systems. Ultimately, the goal is to create AI systems that are both powerful and trustworthy, promoting broader adoption in the healthcare sector while ensuring ethical and safe outcomes for patients.

AI and Cloud Computing in Healthcare: Transforming Patient Care with AWS and Java

A new era in healthcare is being ushered in by the convergence of artificial intelligence (AI) and cloud computing, which will significantly improve patient care through improved diagnosis, individualized treatment planning, and effective patient management. This article explores the potent combination of Java and Amazon Web Services (AWS) and how these technologies may be used to develop cutting-edge AI-driven healthcare solutions that optimize healthcare operations and dramatically enhance patient outcomes. Together with Java's robust and flexible programming environment, healthcare providers can leverage AWS's scalable infrastructure and advanced machine learning capabilities to create and implement sophisticated AI models that analyze massive amounts of medical data at a speed and accuracy never seen before. These developments are making it possible to make more accurate diagnoses, customized treatment plans, and proactive patient monitoring, which can result in better clinical judgment and possibly life-saving measures. But even as we consider the enormous positive effects these technologies have on society, we also have to face the ethical problems they raise, such as issues with data privacy, potential biases in algorithms, and unequal access to cutting-edge medical treatments.

AI and Cloud Computing in Healthcare: Transforming Patient Care with AWS and Java

A new era in healthcare is being ushered in by the convergence of artificial intelligence (AI) and cloud computing, which will significantly improve patient care through improved diagnosis, individualized treatment planning, and effective patient management. This article explores the potent combination of Java and Amazon Web Services (AWS) and how these technologies may be used to develop cutting-edge AI-driven healthcare solutions that optimize healthcare operations and dramatically enhance patient outcomes. Together with Java's robust and flexible programming environment, healthcare providers can leverage AWS's scalable infrastructure and advanced machine learning capabilities to create and implement sophisticated AI models that analyze massive amounts of medical data at a speed and accuracy never seen before. These developments are making it possible to make more accurate diagnoses, customized treatment plans, and proactive patient monitoring, which can result in better clinical judgment and possibly life-saving measures. But even as we consider the enormous positive effects these technologies have on society, we also have to face the ethical problems they raise, such as issues with data privacy, potential biases in algorithms, and unequal access to cutting-edge medical treatments. The goal of this thorough investigation is to present a fair assessment of the revolutionary potential of artificial intelligence (AI) and cloud computing in the healthcare industry, while also discussing the wider ramifications and obligations associated with incorporating these formidable technologies into our healthcare systems.