Mona Albadawy

Academic writing is an essential component of research, characterized by structured expression of ideas, data-driven arguments, and logical reasoning. However, it poses challenges such as handling vast amounts of information and complex ideas. The integration of Artificial Intelligence (AI) into academic writing has become increasingly important, offering solutions to these challenges. This review aims to explore specific domains where AI significantly supports academic writing. A systematic review of literature from databases like PubMed, Embase, and Google Scholar, published since 2019, was conducted. Studies were included based on relevance to AI's application in academic writing and research, focusing on writing assistance, grammar improvement, structure optimization, and other related aspects. The search identified 24 studies through which six core domains were identified where AI helps academic writing and research: 1) facilitating idea generation and research design, 2) improving content and structuring, 3) supporting literature review and synthesis, 4) enhancing data management and analysis, 5) supporting editing, review, and publishing, and 6) assisting in communication, outreach, and ethical compliance. ChatGPT has shown substantial potential in these areas, though challenges like maintaining academic integrity and balancing AI use with human insight remain. AI significantly revolutionises academic writing and research across various domains. Recommendations include broader integration of AI tools in research workflows, emphasizing ethical and transparent use, providing adequate training for researchers, and maintaining a balance between AI utility and human insight. Ongoing research and development are essential to address emerging challenges and ethical considerations in AI's application in academia.

This review evaluates the role of Artificial Intelligence (AI) in transforming diagnostic imaging in healthcare. AI has the potential to enhance accuracy and efficiency of interpreting medical images like X-rays, MRIs, and CT scans. A comprehensive literature search across databases like PubMed, Embase, and Google Scholar was conducted, focusing on articles published in peer-reviewed journals in English language since 2019. Inclusion criteria targeted studies on AI's application in diagnostic imaging, while exclusion criteria filtered out irrelevant or empirically unsupported studies. Through 30 included studies, the review identifies four AI domains and eight functions in diagnostic imaging: 1) In the area of Image Analysis and Interpretation, AI capabilities enhanced image analysis, spotting minor discrepancies and anomalies, and by reducing human error, maintaining accuracy and mitigating the impact of fatigue or oversight, 2) The Operational Efficiency is enhanced by AI through efficiency and speed, which accelerates the diagnostic process, and cost-effectiveness, reducing healthcare costs by improving efficiency and accuracy, 3) Predictive and Personalised Healthcare benefit from AI through predictive analytics, leveraging historical data for early diagnosis, and personalised medicine, which employs patient-specific data for tailored diagnostic approaches, 4) Lastly, in Clinical Decision Support, AI assists in complex procedures by providing precise imaging support and integrates with other technologies like electronic health records for enriched health insights, showcasing ai's transformative potential in diagnostic imaging. The review also discusses challenges in AI integration, such as ethical concerns, data privacy, and the need for technology investments and training. AI is revolutionising diagnostic imaging by improving accuracy, efficiency, and personalised healthcare delivery. Recommendations include continued investment in AI, establishment of ethical guidelines, training for healthcare professionals, and ensuring patient-centred AI development. The review calls for collaborative efforts to integrate AI in clinical practice effectively and address healthcare disparities.

Clinical prediction is integral to modern healthcare, leveraging current and historical medical data to forecast health outcomes. The integration of Artificial Intelligence (AI) in this field significantly enhances diagnostic accuracy, treatment planning, disease prevention, and personalised care leading to better patient outcomes and healthcare efficiency. This systematic review implemented a structured four-step methodology, including an extensive literature search in academic databases (PubMed, Embase, Google Scholar), applying specific inclusion and exclusion criteria, data extraction focusing on AI techniques and their applications in clinical prediction, and a thorough analysis of the collected information to understand AI's roles in enhancing clinical prediction. Through the analysis of 74 experimental studies, eight key domains, where AI significantly enhances clinical prediction, were identified: 1) Diagnosis and early detection of disease; 2) Prognosis of disease course and outcomes; 3) Risk assessment of future disease; 4) Treatment response for personalised medicine; 5) Disease progression; 6) Readmission risks; 7) Complication risks; and 8) Mortality prediction. Oncology and radiology come on top of the specialties benefiting from AI in clinical prediction. The review highlights AI's transformative impact across various clinical prediction domains, including its role in revolutionising diagnostics, improving prognosis accuracy, aiding in personalised medicine, and enhancing patient safety. AI-driven tools contribute significantly to the efficiency and effectiveness of healthcare delivery. AI's integration in clinical prediction marks a substantial advancement in healthcare. Recommendations include enhancing data quality and accessibility, promoting interdisciplinary collaboration, focusing on ethical AI practices, investing in AI education, expanding clinical trials, developing regulatory oversight, involving patients in the AI integration process, and continuous monitoring and improvement of AI systems.

• The study systematically examines the role of AI in enhancing CDS, highlighting its impact on patient outcomes and healthcare efficiency. • 32 recent studies were analysed, and six domains were identified; data-driven insights and Analytics, diagnostic and predictive Modelling, treatment optimisation and personalised Medicine, patient monitoring and telehealth Integration, workflow and administrative Efficiency, and knowledge management and decision support. • Despite all benefits, AI faces challenges like data privacy concerns, ethical issues, and difficulties integrating with existing healthcare systems, necessitating multi-disciplinary collaboration. • AI's role in healthcare is transformative, enhancing CDS to provide more effective, efficient, and patient-focused care. • The future of AI in healthcare involves ethical development, ongoing training for healthcare professionals, and collaborative problem-solving, ensuring a balanced integration of AI and human expertise. Artificial Intelligence (AI) is a transformative force in clinical decision support (CDS) systems within healthcare. Its emergence, fuelled by the growing volume and diversity of healthcare data, offers significant potential in patient care, diagnosis, treatment, and health management. This study systematically reviews AI's role in enhancing CDS across six domains, underscoring its impact on patient outcomes and healthcare efficiency. A four-step systematic review was conducted, involving a comprehensive literature search, application of inclusion and exclusion criteria, data extraction and synthesis, and analysis. Sources included PubMed, Embase, and Google Scholar, with papers published in English since 2019. Selected studies focused on AI's application in CDS, with 32 papers ultimately reviewed. The review identified six AI CDS domains: Data-Driven Insights and Analytics, Diagnostic and Predictive Modelling, Treatment Optimisation and Personalised Medicine, Patient Monitoring and Telehealth Integration, Workflow and Administrative Efficiency, and Knowledge Management and Decision Support. Each domain is crucial in improving various aspects of CDS, from enhancing diagnostic accuracy to optimising resource management. AI's capabilities in EHR analysis, predictive analytics, personalised treatment, and telehealth demonstrate its critical role in advancing healthcare. AI significantly enhances healthcare by improving diagnostic precision, predictive capabilities, and administrative efficiency. It facilitates personalised medicine, remote monitoring, and evidence-based decision-making. However, challenges such as data privacy, ethical considerations, and integration with existing systems persist. This requires collaboration among technologists, healthcare professionals, and policymakers. AI is revolutionising healthcare by enhancing CDS in several domains, contributing to more efficient, effective, and patient-centric care. However, it should complement, not replace, human expertise. Future directions include ethical AI development, continuous professional development for healthcare personnel, and collaborative efforts to address challenges. This approach ensures AI's potential is fully harnessed, leading to a synergistic blend of technology and human care.

Diabetes, a major cause of premature mortality, affects millions globally, with its prevalence increasing due to lifestyle factors and aging populations. This systematic review explores the role of Artificial Intelligence (AI) in enhancing the prevention, diagnosis, and management of diabetes, highlighting the potential for personalised and proactive healthcare. A structured four-step method was used, including extensive literature searches, specific inclusion and exclusion criteria, data extraction from selected studies focusing on AI's role in diabetes, and thorough analysis to identify specific domains and functions where AI contributes significantly. Through examining 43 experimental studies, AI has been identified as a transformative force across eight key domains in diabetes care: 1) Diabetes Management and Treatment, 2) Diagnostic and Imaging Technologies, 3) Health Monitoring Systems, 4) Developing Predictive Models, 5) Public Health Interventions, 6) Lifestyle and Dietary Management, 7) Enhancing Clinical Decision-Making, and 8) Patient Engagement and Self-Management. Each domain showcases AI's potential to revolutionise care, from personalising treatment plans and improving diagnostic accuracy to enhancing patient engagement and predictive healthcare. AI's integration into diabetes care offers personalised, efficient, and proactive solutions. It enhances care accuracy, empowers patients, and provides better understanding of diabetes management. However, the successful implementation of AI requires continued research, data security, interdisciplinary collaboration, and a focus on patient-centred solutions. Education for healthcare professionals and regulatory frameworks are also crucial to address challenges like algorithmic bias and ethics. AI in diabetes care promises improved health outcomes and quality of life through personalised and proactive healthcare. Future efforts should focus on continued investment, ensuring data security, fostering interdisciplinary collaboration, and prioritising patient-centred solutions. Regular monitoring and evaluation are essential to adjust strategies and understand long-term impacts, ensuring AI's ethical and effective integration into healthcare.