Testing Healthcare AI Algorithms with Quantum Computing: Enhancing Validation and Accuracy (Published)
Due to its capacity to handle information in fundamentally new ways, leading to computational powers that were previously unreachable, the multidisciplinary subject of quantum computing has recently grown and attracted significant interest from both academia and industry. Quantum computing has great promise, but how exactly it will change healthcare is still largely unknown. The potential of quantum computing to transform compute-intensive healthcare tasks like drug discovery, personalized medicine, DNA sequencing, medical imaging, and operational optimization is the primary focus of this survey paper, which offers the first comprehensive analysis of quantum computing’s diverse capabilities in improving healthcare systems. A new era in healthcare is on the horizon, thanks to quantum computing and AI coming together to transform complicated biological simulations, the processing of genetic data, and advances in drug development. Biological data may be extremely large and complicated, making it difficult for traditional computing tools to handle. This slows down and impairs the accuracy of medical discoveries. Combining the predictive power of AI with the exponential processing speed of quantum computers presents a game-changing opportunity to speed up biological research and clinical applications. The function of quantum machine learning in improving drug discovery molecular dynamics simulations powered by artificial intelligence is discussed in this article. Quickly modeling chemical interactions, analyzing drug-receptor binding affinities, and predicting pharmacokinetics with extraordinary precision are all possible with quantum-enhanced algorithms. To further improve disease progression prediction and therapeutic target identification, we also investigate quantum-assisted deep learning models for understanding complex biological processes like protein folding, epigenetic changes, and connections between metabolic pathways.
Keywords: AI, CNN, Healthcare, quantum computing, reinforcement learning
AI and Human AI Collaboration in Oracle Cloud Technologies for Integration and Process Automation (Published)
Integrating Artificial Intelligence (AI) into cloud-based platforms rapidly transforms how organizations approach integration and process automation. Oracle Cloud Technologies are at the forefront of this evolution, embedding AI capabilities within their Integration Cloud and Process Automation services. This report delves into the current landscape of AI within these Oracle offerings, explores the burgeoning concept of human-AI collaboration, and analyzes the potential benefits and inherent challenges. The synergy between human expertise and AI capabilities promises to unlock unprecedented levels of efficiency, accuracy, and innovation in managing complex business processes and connecting disparate systems. This report highlights Oracle’s current implementations, future vision, and the critical role of human oversight in ensuring the responsible and effective adoption of AI in cloud-based integration and process automation. Key findings indicate that while AI is already enhancing areas like data mapping and document processing, the future roadmap emphasizes generative AI and AI agents to automate intricate workflows further. However, realizing the full potential necessitates addressing challenges related to data quality, integration complexity, and ethical considerations, underscoring the indispensable role of human-AI collaboration.
Keywords: AI, Human-AI collaboration, Integration, oracle cloud technologies, process automation
Intelligent Horizons: Navigating the Benefits and Boundaries of AI-Driven Telemedicine (Published)
Telemedicine and artificial intelligence (AI) integration has revolutionized the healthcare system through accurate diagnosis, effective treatment, and remote consultations. Some of the technologies used in AI include machine learning algorithms and natural language processing technology, which help algorithms offer predictive analytics and personalized care. In addition, these technologies have reduced the clinical staff’s work burden and have led to increased patient engagement. However, despite these skyrocketing forward movements, AI-driven telemedicine faces challenges such as data privacy threats, bias in algorithm use, and the absence of harmonization between different platforms. Implementing these limitations is among the most significant factors that make telehealth services ethical, fair, and scalable. It is therefore essential to analyze the new role of AI in telemedicine, list the advantages and possible risks, and provide strategic recommendations for addressing current challenges. The findings hope to enlighten healthcare executives, legislators, and researchers on the opportunities and challenges of AI in the telemedicine sector.
Keywords: AI, Data Privacy, Digital healthcare, Patient outcomes, predictive analytics, telemedicine
A Comprehensive Framework for Strengthening USA Financial Cybersecurity: Integrating Machine Learning and AI in Fraud Detection Systems (Published)
Financial cybersecurity is of paramount importance in today’s digital age, particularly in the United States, where the financial sector plays a crucial role in the global economy. With the increasing frequency and sophistication of cyber threats, traditional fraud detection systems are facing significant challenges in keeping pace with evolving risks. This paper presents a comprehensive framework for strengthening US financial cybersecurity by integrating machine learning (ML) and artificial intelligence (AI) techniques into fraud detection systems. The framework begins with an exploration of the fundamental concepts of financial cybersecurity, highlighting key threats and regulatory considerations. It then delves into the fundamentals of ML and AI, discussing their applications in fraud detection and the associated benefits and limitations. The design of the framework encompasses data collection, preprocessing, feature engineering, model selection, and integration with existing systems, emphasizing scalability and adaptability. Through case studies and best practices, the paper illustrates successful implementations of ML/AI in financial cybersecurity and draws lessons from real-world applications. Ethical and privacy considerations are addressed, emphasizing the importance of ethical guidelines, privacy protection, and regulatory compliance. Looking to the future, the paper discusses emerging trends in cyber threats and advancements in ML/AI technologies, while also acknowledging anticipated challenges. In conclusion, the framework outlined in this paper offers a holistic approach to enhancing US financial cybersecurity, emphasizing the critical role of ML and AI in mitigating cyber risks and safeguarding financial institutions and their customers. Recommendations for future research and implementation efforts are provided to further strengthen the resilience of financial systems against evolving cyber threats.
Keywords: AI, Framework, US financial cybersecurity, fraud detection systems., integrating machine learning, strengthening
Application of Expert System for Diagnosing Medical Conditions: A Methodological Review (Published)
Naturally, human diseases should be treated on time; otherwise the patients might die if there is delay in attending to such patient or scarcity of medical practitioners’ or experts. Several attempts have been made through studies to design and built software based medical expert systems for probing and prognosis of several medical conditions using artificial and non-artificial based approaches for patients and medical facilities. This paper represents a comprehensive methodological review of existing medical expert systems used for diagnosis of various diseases based on the increasing demand of expert systems to support the human experts. The study provides a concise evaluation of the various techniques used such as rule-based, fuzzy, artificial neural networks and intelligent hybrid models. The rule-based techniques is not too efficient based on its inability to learn and require powerful search strategies for its knowledge-base; while the fuzzy or ANN models are less efficient when compared to the hybrid models that can give a more accurate results.
Keywords: AI, ANN, Expert System, Fuzzy Logic, Intelligent hybrid model, Rule-based