Advancing Patient Care: How Artificial Intelligence Is Transforming Healthcare

AI-driven innovations in Alzheimer's disease: Integrating early diagnosis, personalized treatment, and prognostic modelling

Alzheimer's disease (AD) presents a significant challenge in neurodegenerative research and clinical practice due to its complex etiology and progressive nature. The integration of artificial intelligence (AI) into the diagnosis, treatment, and prognostic modelling of AD holds promising potential to transform the landscape of dementia care. This review explores recent advancements in AI applications across various stages of AD management. In early diagnosis, AI-enhanced neuroimaging techniques, including MRI, PET, and CT scans, enable precise detection of AD biomarkers. Machine learning models analyze these images to identify patterns indicative of early cognitive decline. Additionally, AI algorithms are employed to detect genetic and proteomic biomarkers, facilitating early intervention. Cognitive and behavioral assessments have also benefited from AI, with tools that enhance the accuracy of neuropsychological tests and analyze speech and language patterns for early signs of dementia. Personalized treatment strategies have been revolutionized by AI-driven approaches. In drug discovery, virtual screening and drug repurposing, guided by predictive modelling, accelerate the identification of effective treatments. AI also aids in tailoring therapeutic interventions by predicting individual responses to treatments and monitoring patient progress, allowing for dynamic adjustment of care plans. Prognostic modelling, another critical area, utilizes AI to predict disease progression through longitudinal data analysis and risk prediction models. The integration of multi-modal data, combining clinical, genetic, and imaging information, enhances the accuracy of these predictions. Deep learning techniques are particularly effective in fusing diverse data types to uncover new insights into disease mechanisms and progression. Despite these advancements, challenges remain, including ethical considerations, data privacy, and the need for seamless integration of AI tools into clinical workflows. This review underscores the transformative potential of AI in AD management while highlighting areas for future research and development. By leveraging AI, the healthcare community can improve early diagnosis, personalize treatments, and predict disease outcomes more accurately, ultimately enhancing the quality of life for individuals with AD.

Medical students' perceptions of an artificial intelligence (AI) assisted diagnosing program

As artificial intelligence (AI) assisted diagnosing systems become accessible and user-friendly, evaluating how first-year medical students perceive such systems holds substantial importance in medical education. This study aimed to assess medical students' perceptions of an AI-assisted diagnostic tool known as 'Glass AI.' Data was collected from first year medical students enrolled in a 1.5-week Cell Physiology pre-clerkship unit. Students voluntarily participated in an activity that involved implementation of Glass AI to solve a clinical case. A questionnaire was designed using 3 domains: 1) immediate experience with Glass AI, 2) potential for Glass AI utilization in medical education, and 3) student deliberations of AI-assisted diagnostic systems for future healthcare environments. 73/202 (36.10%) of students completed the survey. 96% of the participants noted that Glass AI increased confidence in the diagnosis, 43% thought Glass AI lacked sufficient explanation, and 68% expressed risk concerns for the physician workforce. Students expressed future positive outlooks involving AI-assisted diagnosing systems in healthcare, provided strict regulations, are set to protect patient privacy and safety, address legal liability, remove system biases, and improve quality of patient care. In conclusion, first year medical students are aware that AI will play a role in their careers as students and future physicians.

Future Horizons: The Potential Role of Artificial Intelligence in Cardiology

Cardiovascular diseases (CVDs) are the leading cause of premature death and disability globally, leading to significant increases in healthcare costs and economic strains. Artificial intelligence (AI) is emerging as a crucial technology in this context, promising to have a significant impact on the management of CVDs. A wide range of methods can be used to develop effective models for medical applications, encompassing everything from predicting and diagnosing diseases to determining the most suitable treatment for individual patients. This literature review synthesizes findings from multiple studies that apply AI technologies such as machine learning algorithms and neural networks to electrocardiograms, echocardiography, coronary angiography, computed tomography, and cardiac magnetic resonance imaging. A narrative review of 127 articles identified 31 papers that were directly relevant to the research, encompassing a broad spectrum of AI applications in cardiology. These applications included AI models for ECG, echocardiography, coronary angiography, computed tomography, and cardiac MRI aimed at diagnosing various cardiovascular diseases such as coronary artery disease, hypertrophic cardiomyopathy, arrhythmias, pulmonary embolism, and valvulopathies. The papers also explored new methods for cardiovascular risk assessment, automated measurements, and optimizing treatment strategies, demonstrating the benefits of AI technologies in cardiology. In conclusion, the integration of artificial intelligence (AI) in cardiology promises substantial advancements in diagnosing and treating cardiovascular diseases.

In-depth analysis of ChatGPT's performance based on specific signaling words and phrases in the question stem of 2377 USMLE step 1 style questions

ChatGPT has garnered attention as a multifaceted AI chatbot with potential applications in medicine. Despite intriguing preliminary findings in areas such as clinical management and patient education, there remains a substantial knowledge gap in comprehensively understanding the chances and limitations of ChatGPT's capabilities, especially in medical test-taking and education. A total of n = 2,729 USMLE Step 1 practice questions were extracted from the Amboss question bank. After excluding 352 image-based questions, a total of 2,377 text-based questions were further categorized and entered manually into ChatGPT, and its responses were recorded. ChatGPT's overall performance was analyzed based on question difficulty, category, and content with regards to specific signal words and phrases. ChatGPT achieved an overall accuracy rate of 55.8% in a total number of n = 2,377 USMLE Step 1 preparation questions obtained from the Amboss online question bank. It demonstrated a significant inverse correlation between question difficulty and performance with rs = -0.306; p < 0.001, maintaining comparable accuracy to the human user peer group across different levels of question difficulty. Notably, ChatGPT outperformed in serology-related questions (61.1% vs. 53.8%; p = 0.005) but struggled with ECG-related content (42.9% vs. 55.6%; p = 0.021). ChatGPT achieved statistically significant worse performances in pathophysiology-related question stems. (Signal phrase = "what is the most likely/probable cause"). ChatGPT performed consistent across various question categories and difficulty levels. These findings emphasize the need for further investigations to explore the potential and limitations of ChatGPT in medical examination and education.

Advancing Health Equity Through Artificial Intelligence: An Educational Framework for Preparing Nurses in Clinical Practice and Research

The integration of artificial intelligence (AI) into health care offers the potential to enhance patient care, improve diagnostic precision, and broaden access to health-care services. Nurses, positioned at the forefront of patient care, play a pivotal role in utilizing AI to foster a more efficient and equitable health-care system. However, to fulfil this role, nurses will require education that prepares them with the necessary skills and knowledge for the effective and ethical application of AI. This article proposes a framework for nurses which includes AI principles, skills, competencies, and curriculum development focused on the practical use of AI, with an emphasis on care that aims to achieve health equity. By adopting this educational framework, nurses will be prepared to make substantial contributions to reducing health disparities and fostering a health-care system that is more efficient and equitable.

Scope and caveats: Artificial intelligence in gastroenterology

The use of Artificial intelligence (AI) has evolved from its mid-20^th century origins to playing a pivotal tool in modern medicine. It leverages digital data and computational hardware for diverse applications, including diagnosis, prognosis, and treatment responses in gastrointestinal and hepatic conditions. AI has had an impact in diagnostic techniques, particularly endoscopy, ultrasound, and histopathology. AI encompasses machine learning, natural language processing, and robotics, with machine learning being central. This involves sophisticated algorithms capable of managing complex datasets, far surpassing traditional statistical methods. These algorithms, both supervised and unsupervised, are integral for interpreting large datasets. In liver diseases, AI's non-invasive diagnostic applications, particularly in non-alcoholic fatty liver disease, and its role in characterizing hepatic lesions is promising. AI aids in distinguishing between normal and cirrhotic livers and improves the accuracy of lesion characterization and prognostication of hepatocellular carcinoma. AI enhances lesion identification during endoscopy, showing potential in the diagnosis and management of early-stage esophageal carcinoma. In peptic ulcer disease, AI technologies influence patient management strategies. AI is useful in colonoscopy, particularly in detecting smaller colonic polyps. However, its applicability in non-academic settings requires further validation. Addressing these issues is vital for harnessing the potential of AI. In conclusion, while AI offers transformative possibilities in gastroenterology, careful integration and balancing of technical possibilities with ethical and practical application, is essential for optimal use.

A Systematic Review of the Application of Artificial Intelligence in Nursing Care: Where are We, and What's Next?

Background

Integrating Artificial Intelligence (AI) into healthcare has transformed the landscape of patient care and healthcare delivery. Despite this, there remains a notable gap in the existing literature synthesizing the comprehensive understanding of AI's utilization in nursing care.

Objective

This systematic review aims to synthesize the available evidence to comprehensively understand the application of AI in nursing care.

Methods

Studies published between January 2019 and December 2023, identified through CINAHL Plus with Full Text, Web of Science, PubMed, and Medline, were included in this review. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines guided the identification, screening, exclusion, and inclusion of articles. The convergent integrated analysis framework, as proposed by the Joanna Briggs Institute, was employed to synthesize data from the included studies for theme generation.

Results

A total of 337 records were identified from databases. Among them, 35 duplicates were removed, and 302 records underwent eligibility screening. After applying inclusion and exclusion criteria, eleven studies were deemed eligible and included in this review. Through data synthesis of these studies, six themes pertaining to the use of AI in nursing care were identified: 1) Risk Identification, 2) Health Assessment, 3) Patient Classification, 4) Research Development, 5) Improved Care Delivery and Medical Records, and 6) Developing a Nursing Care Plan.

Conclusion

This systematic review contributes valuable insights into the multifaceted applications of AI in nursing care. Through the synthesis of data from the included studies, six distinct themes emerged. These findings not only consolidate the current knowledge base but also underscore the diverse ways in which AI is shaping and improving nursing care practices.

Colonialism in the new digital health agenda

The advancement of digital technologies has stimulated immense excitement about the possibilities of transforming healthcare, especially in resource-constrained contexts. For many, this rapid growth presents a 'digital health revolution'. While this is true, there are also dangers that the proliferation of digital health in the global south reinforces existing colonialities. Underpinned by the rhetoric of modernity, rationality and progress, many countries in the global south are pushing for digital health transformation in ways that ignore robust regulation, increase commercialisation and disregard local contexts, which risks heightened inequalities. We propose a decolonial agenda for digital health which shifts the liner and simplistic understanding of digital innovation as the magic wand for health justice. In our proposed approach, we argue for both conceptual and empirical reimagination of digital health agendas in ways that centre indigenous and intersectional theories. This enables the prioritisation of local contexts and foregrounds digital health regulatory infrastructures as a possible site of both struggle and resistance. Our decolonial digital health agenda critically reflects on who is benefitting from digital health systems, centres communities and those with lived experiences and finally introduces robust regulation to counter the social harms of digitisation.

The Role of Artificial Intelligence in Improving Patient Outcomes and Future of Healthcare Delivery in Cardiology: A Narrative Review of the Literature

Cardiovascular diseases exert a significant burden on the healthcare system worldwide. This narrative literature review discusses the role of artificial intelligence (AI) in the field of cardiology. AI has the potential to assist healthcare professionals in several ways, such as diagnosing pathologies, guiding treatments, and monitoring patients, which can lead to improved patient outcomes and a more efficient healthcare system. Moreover, clinical decision support systems in cardiology have improved significantly over the past decade. The addition of AI to these clinical decision support systems can improve patient outcomes by processing large amounts of data, identifying subtle associations, and providing a timely, evidence-based recommendation to healthcare professionals. Lastly, the application of AI allows for personalized care by utilizing predictive models and generating patient-specific treatment plans. However, there are several challenges associated with the use of AI in healthcare. The application of AI in healthcare comes with significant cost and ethical considerations. Despite these challenges, AI will be an integral part of healthcare delivery in the near future, leading to personalized patient care, improved physician efficiency, and anticipated better outcomes.

Transformative Potential of AI in Healthcare: Definitions, Applications, and Navigating the Ethical Landscape and Public Perspectives

Artificial intelligence (AI) has emerged as a crucial tool in healthcare with the primary aim of improving patient outcomes and optimizing healthcare delivery. By harnessing machine learning algorithms, natural language processing, and computer vision, AI enables the analysis of complex medical data. The integration of AI into healthcare systems aims to support clinicians, personalize patient care, and enhance population health, all while addressing the challenges posed by rising costs and limited resources. As a subdivision of computer science, AI focuses on the development of advanced algorithms capable of performing complex tasks that were once reliant on human intelligence. The ultimate goal is to achieve human-level performance with improved efficiency and accuracy in problem-solving and task execution, thereby reducing the need for human intervention. Various industries, including engineering, media/entertainment, finance, and education, have already reaped significant benefits by incorporating AI systems into their operations. Notably, the healthcare sector has witnessed rapid growth in the utilization of AI technology. Nevertheless, there remains untapped potential for AI to truly revolutionize the industry. It is important to note that despite concerns about job displacement, AI in healthcare should not be viewed as a threat to human workers. Instead, AI systems are designed to augment and support healthcare professionals, freeing up their time to focus on more complex and critical tasks. By automating routine and repetitive tasks, AI can alleviate the burden on healthcare professionals, allowing them to dedicate more attention to patient care and meaningful interactions. However, legal and ethical challenges must be addressed when embracing AI technology in medicine, alongside comprehensive public education to ensure widespread acceptance.

Personalized Treatments Based on Laryngopharyngeal Reflux Patient Profiles: A Narrative Review

OBJECTIVE

To review the current findings of the literature on the existence of several profiles of laryngopharyngeal reflux (LPR) patients and to propose personalized diagnostic and therapeutic approaches.

METHODS

A state-of-the art review of the literature was conducted using the PubMED, Scopus, and Cochrane Library databases. The information related to epidemiology, demographics, clinical presentations, diagnostic approaches, and therapeutic responses were extracted to identify outcomes that may influence the clinical and therapeutic courses of LPR.

RESULTS

The clinical presentation and therapeutic courses of LPR may be influenced by gender, age, weight, comorbidities, dietary habits and culture, anxiety, stress, and saliva enzyme profile. The clinical expression of reflux, including laryngopharyngeal, respiratory, nasal, and eye symptoms, and the hypopharyngeal-esophageal multichannel intraluminal impedance-pH monitoring profile of patients are important issues to improve in patient management. The use of more personalized therapeutic strategies appears to be associated with better symptom relief and cures over the long-term. The role of pepsin in LPR physiology is well-established but the lack of information about the role of other gastrointestinal enzymes in the development of LPR-related mucosa inflammation limits the development of future enzyme-based personalized diagnostic and therapeutic approaches.

CONCLUSION

Laryngopharyngeal reflux is a challenging ear, nose, and throat condition associated with poor therapeutic responses and a long-term burden in Western countries. Artificial intelligence should be used for developing personalized therapeutic strategies based on patient features.

Enhancing the Evidence with Algorithms: How Artificial Intelligence Is Transforming Forensic Medicine

BACKGROUND

The integration of artificial intelligence (AI) into various fields has ushered in a new era of multidisciplinary progress. Defined as the ability of a system to interpret external data, learn from it, and adapt to specific tasks, AI is poised to revolutionize the world. In forensic medicine and pathology, algorithms play a crucial role in data analysis, pattern recognition, anomaly identification, and decision making. This review explores the diverse applications of AI in forensic medicine, encompassing fields such as forensic identification, ballistics, traumatic injuries, postmortem interval estimation, forensic toxicology, and more.

RESULTS

A thorough review of 113 articles revealed a subset of 32 papers directly relevant to the research, covering a wide range of applications. These included forensic identification, ballistics and additional factors of shooting, traumatic injuries, post-mortem interval estimation, forensic toxicology, sexual assaults/rape, crime scene reconstruction, virtual autopsy, and medical act quality evaluation. The studies demonstrated the feasibility and advantages of employing AI technology in various facets of forensic medicine and pathology.

CONCLUSIONS

The integration of AI in forensic medicine and pathology offers promising prospects for improving accuracy and efficiency in medico-legal practices. From forensic identification to post-mortem interval estimation, AI algorithms have shown the potential to reduce human subjectivity, mitigate errors, and provide cost-effective solutions. While challenges surrounding ethical considerations, data security, and algorithmic correctness persist, continued research and technological advancements hold the key to realizing the full potential of AI in forensic applications. As the field of AI continues to evolve, it is poised to play an increasingly pivotal role in the future of forensic medicine and pathology.