Papers by Shanavaz Mohammed
IMRJR, 2024
The pharmaceutical industry has grown over the past decade especially with the embracing of techn... more The pharmaceutical industry has grown over the past decade especially with the embracing of technology in major parts of their operations. This dependence on the technology aspects has also resulted in increased vulnerability from attacks by hackers and other unauthorized persons on the internet. Various cybersecurity threats such as malware, ransomware, phishing, social engineering, insider threats, advanced persistent threats (APTs), and data breaches, have continuously caused loss of personal data as well as financial loss for companies. This research discusses these major cybersecurity threats that companies need to understand and put up measures to curb any unauthorized access to their systems. The key regulatory framework such as those outlined by HIPAA and FDA, are also explained and how companies can make sure they adhere to such guidelines. A detailed explanation of CISOs functions in pharmaceutical industry companies is also detailed herein.
IARJSET, 2024
Artificial Intelligence (AI) has significantly transformed various industries, including the phar... more Artificial Intelligence (AI) has significantly transformed various industries, including the pharmaceutical sector. As of 2023, the US AI market is valued at $123 billion, with a projection of $594 billion by 2032. Industries such as technology, automotive, finance, and healthcare have seen substantial AI adoption. In healthcare, AI optimizes routine tasks, accelerates drug discovery, and enhances clinical trials and manufacturing processes. However, the increased reliance on AI exposes pharmaceutical companies to cybersecurity threats, including data breaches, intellectual property theft, ransomware, and insider threats. Robust cybersecurity measures, such as strong access controls, securing AI systems, incident response plans, regular security audits, data encryption, employee training, and industry collaboration, are critical. Future trends indicate growing AI investment in healthcare, necessitating continuous advancements in cybersecurity to protect sensitive data and ensure regulatory compliance.
The adoption of technology in the pharmaceutical industry has transformed how healthcare provider... more The adoption of technology in the pharmaceutical industry has transformed how healthcare providers entirely communicate and deal with patients. Telemedicine for instance, has been able to bridge the geographical gap between patients in rural and remote areas and specialist healthcare providers. It has also been able to facilitate enhanced access to care, improve medical management and improved innovative patient education. Telemedicine has also enabled patient counselling especially where patients are critically ill or have chronic diseases. The benefits of telemedicine in the pharmaceutical industry, such as such as increased accessibility for underserved populations, enhanced medication adherence, and improved patient outcomes, cannot be overlooked. Even with the challenges that face organizations with regards to technology use such as data privacy and data theft, there are high hopes to the continued use of technology aspects such as telemedicine and Electronic Health Records (EHRs) in the pharmaceutical industry.
IJIREEICE, 2024
The drug development industry has greatly benefitted from the application of artificial intellige... more The drug development industry has greatly benefitted from the application of artificial intelligence (AI) in genomic data analysis. Genomic data is useful in understanding diseases genetics as well as in making drugs to counter the diseases. However, this process is quite challenging as it requires a lot of data and analysis which makes the process complex. This aspect of the study made the process to be time-consuming and quite resource intensive which necessitated a better and more efficient tool for analysis. AI has advanced machine learning and deep learning algorithms that enable it to be an effective option in the process. AI offers great solutions for processing large sets of data with efficient and accurate outcomes. As such, the drug development industry has benefited from reduced costs, saving time, and accurate and more effective research data. It has also ensured that therapies are tailor-made for the patients, including making appropriate treatments for the specific genetic profile, ensuring better treatment outcomes. It is therefore without doubt that artificial intelligence has been a great transformative force in the industry and continues to facilitate innovation and advancement in the process.
AI-Driven Behavioural Insights for Ozempic Drug Users
JCCCI, 2025
Intelligent Edge Computing for Real-Time Tumor Detection Using Machine Learning
IEEE, 2024
A Comparative Analysis of Convolutional Neural Networks for Brain Cancer Detection
IEEE, 2024
Enhancing Tumor Detection in Medical Imaging Using Deep Learning Techniques
IEEE, 2024
IJARCCE, 2024
The integration of artificial intelligence (AI) in clinical trial management represents a transfo... more The integration of artificial intelligence (AI) in clinical trial management represents a transformative approach in medical research. This study examines the impact of AI on clinical trials, highlighting its ability to automate routine tasks, enhance data accuracy, and improve patient recruitment and monitoring. AI's predictive analytics, natural language processing, and real-time monitoring capabilities significantly increase efficiency, reduce costs, and improve data quality. Despite these advancements, challenges such as standardization and integration with existing systems remain. Overall, AI holds substantial promise for enhancing the effectiveness and patient-centricity of clinical trials.
Ijcsrr, 2024
The diagnosis of rare diseases presents significant challenges due to their low prevalence, compl... more The diagnosis of rare diseases presents significant challenges due to their low prevalence, complex symptomatology, and the scarcity of specialized knowledge. However, advancements in Artificial Intelligence (AI) offer promising solutions to these challenges. This review explores the current state of AI-powered diagnostic tools for rare diseases, focusing on the methodologies, algorithms, and platforms utilized in this emerging field. We examine how AI technologies, such as machine learning, deep learning, and natural language processing, are being integrated into clinical practice to enhance diagnostic accuracy and speed. The research also provides the examples that highlight the successes and limitations of AI in this domain, providing insights into how AI can be harnessed to improve patient outcomes in rare disease diagnosis and management.
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Papers by Shanavaz Mohammed