Artificial Intelligence-Driven Diagnosis of Pancreatic Cancer

Hameed, Bahrudeen Shahul; Krishnan, Uma Maheswari

doi:10.3390/cancers14215382

Cited by 23 publications

(13 citation statements)

References 134 publications

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“…A single academic article [16] stated that data accessibility is hampered by privacy regulations, which require obtaining consent from government bodies and vaccine manufacturers. The significance of data privacy and security becomes paramount as organizations increasingly depend on machine learning models for predicting required stock levels [32]. Data collection is often sourced from several channels, including online purchases, social network engagements, and loyalty schemes [33].…”

Section: Ethical Consideration and Data Privacymentioning

confidence: 99%

Assessing the impact of artificial intelligence and machine learning on forecasting medication demand and supply in public pharmaceutical systems: A systematic review

Tangi Ndakondja Angula,

Abraham Dongo

2024

GSC Biol. Pharm. Sci.

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Background: Effectively managing drug demand and supply through pharmaceutical quantification is critical as it ensures that medications are readily available when needed while reducing costs, optimizing inventory management, and ultimately improving patient care. This research aimed to examine the existing literature on the influence of artificial intelligence (AI) and machine learning (ML) on predicting pharmaceutical demand in public systems. This review focused specifically on the accuracy of these methods, their limitations, and the ethical concerns associated with their use. Methods: The research used PubMed and Google Scholar databases, following PRISMA principles, and yielded 13 peer-reviewed articles. The quality of the included studies was assessed for potential bias using established standard criteria, the Cochrane Risk of Bias Checklist Tool for systematic reviews of intervention. Results: The results show that linear regression and random forest are the predominant models for predicting medication quantities in hospital pharmacies. However, the precision of these models can be affected by data entry inaccuracies and fluctuations. The study identified technical, human, and organizational obstacles as barriers to adoption, as well as problems related to privacy and confidentiality. Conclusion: The use of AI and ML can estimate the demand and supply of medicine in public pharmaceutical delivery systems. The results highlight the importance of further study to improve forecasting algorithm simulation accuracy, broaden single time-series projections to incorporate additional patient-associated factors and investigate various efficiency measures.

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Section: Ethical Consideration and Data Privacymentioning

confidence: 99%

Assessing the impact of artificial intelligence and machine learning on forecasting medication demand and supply in public pharmaceutical systems: A systematic review

Tangi Ndakondja Angula,

Abraham Dongo

2024

GSC Biol. Pharm. Sci.

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“…Early CNNs are used to identify breast lesions. Whereas deep CNN, a DL algorithm, helps identify and classify lesions by studying the images [ 17 ].…”

Section: Reviewmentioning

confidence: 99%

Reimagining Healthcare: Unleashing the Power of Artificial Intelligence in Medicine

Iqbal,

Cortés Jaimes,

Makineni

et al. 2023

Cureus

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Artificial intelligence (AI) has opened new medical avenues and revolutionized diagnostic and therapeutic practices, allowing healthcare providers to overcome significant challenges associated with cost, disease management, accessibility, and treatment optimization. Prominent AI technologies such as machine learning (ML) and deep learning (DL) have immensely influenced diagnostics, patient monitoring, novel pharmaceutical discoveries, drug development, and telemedicine. Significant innovations and improvements in disease identification and early intervention have been made using AI-generated algorithms for clinical decision support systems and disease prediction models. AI has remarkably impacted clinical drug trials by amplifying research into drug efficacy, adverse events, and candidate molecular design. AI’s precision and analysis regarding patients’ genetic, environmental, and lifestyle factors have led to individualized treatment strategies. During the COVID-19 pandemic, AI-assisted telemedicine set a precedent for remote healthcare delivery and patient follow-up. Moreover, AI-generated applications and wearable devices have allowed ambulatory monitoring of vital signs. However, apart from being immensely transformative, AI’s contribution to healthcare is subject to ethical and regulatory concerns. AI-backed data protection and algorithm transparency should be strictly adherent to ethical principles. Vigorous governance frameworks should be in place before incorporating AI in mental health interventions through AI-operated chatbots, medical education enhancements, and virtual reality-based training. The role of AI in medical decision-making has certain limitations, necessitating the importance of hands-on experience. Therefore, reaching an optimal balance between AI’s capabilities and ethical considerations to ensure impartial and neutral performance in healthcare applications is crucial. This narrative review focuses on AI’s impact on healthcare and the importance of ethical and balanced incorporation to make use of its full potential.

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“…Widely utilized models include CNN-based architectures www.co-gastroenterology.com such as UNet and its variations [21]. Transfer learning is often employed to adapt pretrained CNN models, including VGG [22], Inception [23], and ResNet [24], to discern specific features in pancreatic cancer imaging. Despite its promise, deep learning faces challenges such as data scarcity, model interpretability, and generalizability.…”

Section: Basicsmentioning

confidence: 99%

A review of deep learning and radiomics approaches for pancreatic cancer diagnosis from medical imaging

Zhang

Keleş

et al. 2023

Current Opinion in Gastroenterology

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Purpose of review Early and accurate diagnosis of pancreatic cancer is crucial for improving patient outcomes, and artificial intelligence (AI) algorithms have the potential to play a vital role in computer-aided diagnosis of pancreatic cancer. In this review, we aim to provide the latest and relevant advances in AI, specifically deep learning (DL) and radiomics approaches, for pancreatic cancer diagnosis using cross-sectional imaging examinations such as computed tomography (CT) and magnetic resonance imaging (MRI). Recent findings This review highlights the recent developments in DL techniques applied to medical imaging, including convolutional neural networks (CNNs), transformer-based models, and novel deep learning architectures that focus on multitype pancreatic lesions, multiorgan and multitumor segmentation, as well as incorporating auxiliary information. We also discuss advancements in radiomics, such as improved imaging feature extraction, optimized machine learning classifiers and integration with clinical data. Furthermore, we explore implementing AI-based clinical decision support systems for pancreatic cancer diagnosis using medical imaging in practical settings. Summary Deep learning and radiomics with medical imaging have demonstrated strong potential to improve diagnostic accuracy of pancreatic cancer, facilitate personalized treatment planning, and identify prognostic and predictive biomarkers. However, challenges remain in translating research findings into clinical practice. More studies are required focusing on refining these methods, addressing significant limitations, and developing integrative approaches for data analysis to further advance the field of pancreatic cancer diagnosis.

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Artificial Intelligence-Driven Diagnosis of Pancreatic Cancer

Cited by 23 publications

References 134 publications

Assessing the impact of artificial intelligence and machine learning on forecasting medication demand and supply in public pharmaceutical systems: A systematic review

Assessing the impact of artificial intelligence and machine learning on forecasting medication demand and supply in public pharmaceutical systems: A systematic review

Reimagining Healthcare: Unleashing the Power of Artificial Intelligence in Medicine

A review of deep learning and radiomics approaches for pancreatic cancer diagnosis from medical imaging

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