Machine Learning and AI in Cancer Prognosis, Prediction, and Treatment Selection: A Critical Approach

Journal of Multidisciplinary Healthcare, Journal Year: 2023, Volume and Issue: Volume 16, P. 1779 - 1791

Published: June 1, 2023

Cancer is a leading cause of morbidity and mortality worldwide. While progress has been made in the diagnosis, prognosis, treatment cancer patients, individualized data-driven care remains challenge. Artificial intelligence (AI), which used to predict automate many cancers, emerged as promising option for improving healthcare accuracy patient outcomes. AI applications oncology include risk assessment, early prognosis estimation, selection based on deep knowledge. Machine learning (ML), subset that enables computers learn from training data, highly effective at predicting various types cancer, including breast, brain, lung, liver, prostate cancer. In fact, ML have demonstrated greater than clinicians. These technologies also potential improve quality life patients with illnesses, not just Therefore, it important current develop new programs benefit patients. This article examines use algorithms prediction, their applications, limitations, future prospects.

Language: Английский

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Artificial Intelligence in Cancer Research: Trends, Challenges and Future Directions

Life, Journal Year: 2022, Volume and Issue: 12(12), P. 1991 - 1991

Published: Nov. 28, 2022

The World Health Organization (WHO), in their 2022 report, identified cancer as one of the leading causes death, accounting for about 16% deaths worldwide. Cancer-Moonshot community aims to reduce death rate by half next 25 years and wants improve lives cancer-affected people. Cancer mortality can be reduced if detected early treated appropriately. Cancers like breast cervical have high cure probabilities when accordance with best practices. Integration artificial intelligence (AI) into research is currently addressing many challenges where medical experts fail bring control cure, outcomes are quite encouraging. AI offers tools platforms facilitate more understanding tackling this life-threatening disease. AI-based systems help pathologists diagnosing accurately consistently, reducing case error rates. Predictive-AI models estimate likelihood a person get identifying risk factors. Big data, together AI, enable develop customized treatments patients. side effects from kind therapy will less severe comparison generalized therapies. However, these remain ineffective fighting against saving millions patients unless they accessible understandable biologists, oncologists, other researchers. This paper presents trends, challenges, future directions research. We hope that both technical getting better opportunities diagnosis treatment.

Language: Английский

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Improving health literacy using the power of digital communications to achieve better health outcomes for patients and practitioners

Frontiers in Digital Health, Journal Year: 2023, Volume and Issue: 5

Published: Nov. 17, 2023

Digital communication tools have demonstrated significant potential to improve health literacy which ultimately leads better outcomes. In this article, we examine the power of digital such as mobile apps, telemedicine and online information resources promote literacy. We outline evidence that facilitate patient education, self-management empowerment possibilities. addition, technology is optimising for improved clinical decision-making, treatment options among providers. also explore challenges limitations associated with literacy, including issues related access, reliability privacy. propose leveraging key engagement enhance across demographics leading transformation healthcare delivery driving outcomes all.

Language: Английский

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A Review of Deep Transfer Learning Approaches for Class-Wise Prediction of Alzheimer’s Disease Using MRI Images

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 30(4), P. 2409 - 2429

Published: Jan. 3, 2023

Language: Английский

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From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer

Diagnostics, Journal Year: 2024, Volume and Issue: 14(2), P. 174 - 174

Published: Jan. 12, 2024

Pancreatic cancer is a highly aggressive and difficult-to-detect with poor prognosis. Late diagnosis common due to lack of early symptoms, specific markers, the challenging location pancreas. Imaging technologies have improved diagnosis, but there still room for improvement in standardizing guidelines. Biopsies histopathological analysis are tumor heterogeneity. Artificial Intelligence (AI) revolutionizes healthcare by improving treatment, patient care. AI algorithms can analyze medical images precision, aiding disease detection. also plays role personalized medicine analyzing data tailor treatment plans. It streamlines administrative tasks, such as coding documentation, provides assistance through chatbots. However, challenges include privacy, security, ethical considerations. This review article focuses on potential transforming pancreatic care, offering diagnostics, treatments, operational efficiency, leading better outcomes.

Language: Английский

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Advancing cancer diagnosis and treatment: integrating image analysis and AI algorithms for enhanced clinical practice

Artificial Intelligence Review, Journal Year: 2025, Volume and Issue: 58(4)

Published: Jan. 25, 2025

Language: Английский

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Deep learning techniques for cancer classification using microarray gene expression data

Frontiers in Physiology, Journal Year: 2022, Volume and Issue: 13

Published: Sept. 30, 2022

Cancer is one of the top causes death globally. Recently, microarray gene expression data has been used to aid in cancer's effective and early detection. The use DNA technology uncover information from levels thousands genes enormous promise. technique can determine simultaneously a single experiment. analysis critical many disciplines biological study obtain necessary information. This analyses all research studies focused on optimizing selection for cancer detection using artificial intelligence. One most challenging issues figuring out how extract meaningful massive databases. Deep Learning architectures have performed efficiently numerous sectors are diagnose other chronic diseases assist physicians making medical decisions. In this study, we evaluated results different optimizers RNA sequence dataset. learning algorithm proposed classifies five forms cancer, including kidney renal clear cell carcinoma (KIRC), Breast Invasive Carcinoma (BRCA), lung adenocarcinoma (LUAD), Prostate Adenocarcinoma (PRAD) Colon (COAD). performance like Stochastic gradient descent (SGD), Root Mean Squared Propagation (RMSProp), Adaptive Gradient Optimizer (AdaGrad), Momentum (AdaM). experimental gathered dataset affirm that AdaGrad Adam. Also, done rates decay rates. discusses current advancements deep learning-based optimized feature methods.

Language: Английский

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A review on recent developments in cancer detection using Machine Learning and Deep Learning models

Biomedical Signal Processing and Control, Journal Year: 2022, Volume and Issue: 80, P. 104398 - 104398

Published: Nov. 16, 2022

Language: Английский

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Deep Transfer Learning Approaches to Predict Glaucoma, Cataract, Choroidal Neovascularization, Diabetic Macular Edema, DRUSEN and Healthy Eyes: An Experimental Review

Archives of Computational Methods in Engineering, Journal Year: 2022, Volume and Issue: 30(1), P. 521 - 541

Published: Sept. 4, 2022

Language: Английский

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A Systematic Literature Review of Breast Cancer Diagnosis Using Machine Intelligence Techniques

Archives of Computational Methods in Engineering, Journal Year: 2022, Volume and Issue: 29(6), P. 4401 - 4430

Published: April 11, 2022

Language: Английский

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