Добірка наукової літератури з теми "Interpretability of AI Models for Parkinson's Disease Detection"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Interpretability of AI Models for Parkinson's Disease Detection".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Статті в журналах з теми "Interpretability of AI Models for Parkinson's Disease Detection"
1
Samuel Fanijo, Uyok Hanson, Taiwo Akindahunsi, Idris Abijo, and Tinuade Bolutife Dawotola. "Artificial intelligence-powered analysis of medical images for early detection of neurodegenerative diseases." World Journal of Advanced Research and Reviews 19, no. 2 (August 30, 2023): 1578–87. http://dx.doi.org/10.30574/wjarr.2023.19.2.1432.
Повний текст джерелаАнотація:
Neurodegenerative diseases including Alzheimer's, Parkinson's, and Huntington's offer serious health issues to people all over the world, due to their progressive nature and lack of effective therapies. In order to improve patient outcomes and allow for prompt action to limit the progression of the disease, early identification is essential. With a focus on deep learning methods, this study investigates the use of AI-powered analysis of medical images for the early detection of neurodegenerative disorders. The use of several medical imaging modalities, such as PET, CT, and MRI, in identifying disease biomarkers at an early stage is investigated. The usefulness of deep learning techniques to automate feature extraction, categorize illness states, and track disease progression is highlighted. These techniques include convolutional neural networks [CNNs], recurrent neural networks [RNNs], and generative adversarial networks [GANs]. The study also discusses the difficulties in using AI implementation, including data quality, image variability, and the interpretability of AI models. Furthermore, the study explores possible regulatory and ethical considerations in clinical adoption. It also examines AI's growing role in clinical settings and its ability to work with personalized medicine which present promising opportunities for improving the diagnosis and management neurodegenerative disease. The final section of this paper outlines important future directions for increasing the use of AI in clinical care, including multi-modal fusion and transfer learning.
2
Adeniran, Opeyemi Taiwo, Blessing Ojeme, Temitope Ezekiel Ajibola, Ojonugwa Oluwafemi Ejiga Peter, Abiola Olayinka Ajala, Md Mahmudur Rahman, and Fahmi Khalifa. "Explainable MRI-Based Ensemble Learnable Architecture for Alzheimer’s Disease Detection." Algorithms 18, no. 3 (March 13, 2025): 163. https://doi.org/10.3390/a18030163.
Повний текст джерелаАнотація:
With the advancements in deep learning methods, AI systems now perform at the same or higher level than human intelligence in many complex real-world problems. The data and algorithmic opacity of deep learning models, however, make the task of comprehending the input data information, the model, and model’s decisions quite challenging. This lack of transparency constitutes both a practical and an ethical issue. For the present study, it is a major drawback to the deployment of deep learning methods mandated with detecting patterns and prognosticating Alzheimer’s disease. Many approaches presented in the AI and medical literature for overcoming this critical weakness are sometimes at the cost of sacrificing accuracy for interpretability. This study is an attempt at addressing this challenge and fostering transparency and reliability in AI-driven healthcare solutions. The study explores a few commonly used perturbation-based interpretability (LIME) and gradient-based interpretability (Saliency and Grad-CAM) approaches for visualizing and explaining the dataset, models, and decisions of MRI image-based Alzheimer’s disease identification using the diagnostic and predictive strengths of an ensemble framework comprising Convolutional Neural Networks (CNNs) architectures (Custom multi-classifier CNN, VGG-19, ResNet, MobileNet, EfficientNet, DenseNet), and a Vision Transformer (ViT). The experimental results show the stacking ensemble achieving a remarkable accuracy of 98.0% while the hard voting ensemble reached 97.0%. The findings present a valuable contribution to the growing field of explainable artificial intelligence (XAI) in medical imaging, helping end users and researchers to gain deep understanding of the backstory behind medical image dataset and deep learning model’s decisions.
3
Hamza, Naeem, Nuaman Ahmed, and Naeema Zainaba. "A Comparative Analysis of Traditional and AI-Driven Methods for Disease Detection: Novel Approaches, Methodologies, and Challenges." Journal of Medical Health Research and Psychiatry 01, no. 02 (2024): 01–03. https://doi.org/10.70844/jmhrp.2024.1.2.28.
Повний текст джерелаАнотація:
Background: Accurate and early disease detection is crucial for improving patient outcomes. Traditional methods have relied on manual medical data analysis, which can be labor-intensive and error prone. Methods: This comparative review examines traditional versus AI-driven detection methods, highlighting their applications, advantages, and limitations. We employed PRISMA guidelines to systematically review the literature, using strict inclusion and exclusion criteria to evaluate relevant studies. Results: Our findings suggest that while AI-driven methods outperform traditional approaches in terms of speed and accuracy, challenges such as algorithm interpretability and data quality remain significant barriers. Conclusions: Novel aspects of this study include an in-depth comparison of AI models, their integration into clinical practice, and the challenges of data quality and regulatory frameworks. Overall, AI-driven methods have the potential to revolutionize disease detection, but addressing the challenges of algorithm interpretability and data quality is crucial for their successful integration into clinical practice.
4
Fatima, Shereen, Hidayatullah Shaikh, Attaullah Sahito, and Asadullah Kehar. "A Review of Skin Disease Detection Using Deep Learning." VFAST Transactions on Software Engineering 12, no. 4 (December 31, 2024): 220–38. https://doi.org/10.21015/vtse.v12i4.2022.
Повний текст джерелаАнотація:
Amid increasing concerns about skin diseases exacerbated by climate change or lifestyle, some diseases are undiagnosed or misdiagnosed due to limited healthcare facilities. The worldwide health burden emphasizes the need for innovative diagnostics. This study explores the evolutionary role of deep learning in skin disease detection, providing the most advanced and effective research approaches, model achievements, and dataset usage exclusively. The review adapts data from 30 research papers and many datasets to address imbalanced class and various efficiency factors. The developments in CNN models like MobileNet or EfficientNet, have strengthened computational potential, while hybrid models have accommodated local and global features. Furthermore, Explainable AI (EXI) and augmented datasets have overcome the challenges including noisy, biased datasets and the less interpretable AI models. This study declares the innovative capacity of deep learning in dermatological analysis, highlighting its scalability and performance. Future research is required to consider dataset diversity, interpretability, and incorporating medical metadata to enhance model performances.
5
Hasan Saif, Fatima, Mohamed Nasser Al-Andoli, and Wan Mohd Yaakob Wan Bejuri. "Explainable AI for Alzheimer Detection: A Review of Current Methods and Applications." Applied Sciences 14, no. 22 (November 5, 2024): 10121. http://dx.doi.org/10.3390/app142210121.
Повний текст джерелаАнотація:
Alzheimer’s disease (AD) is the most common cause of dementia, marked by cognitive decline and memory loss. Recently, machine learning and deep learning techniques have introduced promising solutions for improving AD detection through MRI, especially in settings where specialists may not be readily available. These techniques offer the potential to assist general practitioners and non-specialists in busy clinical environments. However, the ‘black box’ nature of many AI techniques makes it challenging for non-expert physicians to fully trust their diagnostic accuracy. In this review, we critically evaluate current explainable AI (XAI) methods applied to AD detection and highlight their limitations. In addition, a new interpretability framework, called “Feature-Augmented”, was theoretically designed to improve model interpretability. This approach remains underexplored, primarily due to the scarcity of explainable AD-specific datasets. Furthermore, we underscore the importance of AI models being accurate and explainable, which enhance diagnostic confidence and patient care outcomes.
6
Rakhi Raghukumar, Aswathi V Nair, Amrutha Raju, Aina S Dcruz, and Susheel George Joseph. "AI Used to Predict Alzheimer’s Disease." International Research Journal on Advanced Engineering and Management (IRJAEM) 2, no. 12 (December 12, 2024): 3647–51. https://doi.org/10.47392/irjaem.2024.0541.
Повний текст джерелаАнотація:
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that leads to cognitive decline and memory loss, severely affecting millions worldwide. Early detection and accurate prediction of Alzheimer's are critical for timely interventions. This paper explores the application of Artificial Intelligence (AI) in predicting Alzheimer's disease, focusing on machine learning (ML) models, neural networks, and deep learning (DL) techniques. By analyzing a combination of neuroimaging data, genetic information, and cognitive test results, AI systems can identify subtle patterns and biomarkers that indicate the onset of AD even before the appearance of clinical symptoms. The paper discusses the integration of AI with brain imaging technologies, such as MRI and PET scans, as well as the role of natural language processing (NLP) in evaluating speech and text patterns. Key challenges such as data quality, interpretability, and the need for large, diverse datasets are also addressed. The potential for AI to enhance diagnostic accuracy and facilitate personalized treatment approaches in Alzheimer’s care is highlighted, along with future directions for research in this field. The results suggest that AI has the capacity to significantly improve early detection and intervention strategies, ultimately advancing the fight against Alzheimer's disease.
7
Ismail Y and Vijaya Kumar Voleti. "A Review on Usage of Artificial Intelligence for Early Detection and Management of Alzheimer's Disease." Journal of Pharma Insights and Research 2, no. 5 (October 4, 2024): 007–13. http://dx.doi.org/10.69613/06tz7453.
Повний текст джерелаАнотація:
Artificial Intelligence (AI) has emerged as a powerful tool in Alzheimer's disease (AD) research and clinical practice. This review discusses about the recent advances in AI applications for AD, focusing on neuroimaging analysis, biomarker discovery, cognitive assessment, and predictive modeling. AI techniques, particularly deep learning algorithms, have significantly improved the accuracy and efficiency of brain imaging interpretation, enabling earlier detection of AD-related structural and functional changes. In biomarker research, AI has accelerated the identification of novel blood-based and CSF markers, potentially leading to less invasive and more cost-effective diagnostic methods. AI-driven cognitive assessment tools, including computerized tests and speech analysis, offer more sensitive measures of cognitive decline. Additionally, AI-based predictive models integrating multiple data types show promise in personalized risk assessment and disease progression forecasting. Despite these advancements, challenges remain in data standardization, model interpretability, and ethical considerations. This review explains about the current state of AI in AD research, its potential impact on patient care, and areas requiring further investigation to fully realize the benefits of AI in combating Alzheimer's disease
8
Paul, Tanmoy, Omiya Hassan, Christina S. McCrae, Syed Kamrul Islam, and Abu Saleh Mohammad Mosa. "An Explainable Fusion of ECG and SpO2-Based Models for Real-Time Sleep Apnea Detection." Bioengineering 12, no. 4 (April 3, 2025): 382. https://doi.org/10.3390/bioengineering12040382.
Повний текст джерелаАнотація:
Obstructive sleep apnea (OSA) is a common disorder characterized by disrupted breathing during sleep, leading to serious health consequences such as daytime fatigue, hypertension, metabolic issues, and cardiovascular disease. Polysomnography (PSG) is the standard diagnostic method but is costly and uncomfortable for patients, which has led to interest in artificial intelligence (AI) for automated OSA detection. To develop an explainable AI model that utilizes electrocardiogram (ECG) and blood oxygen saturation (SpO2) data for real-time apnea detection, providing visual explanations to enhance interpretability and support clinical decisions. It emphasizes giving visual explanations to show how specific segments of the signal contribute to the AI’s conclusions. Furthermore, it explores the combination of individual models to improve detection accuracy. The fusion of individual models demonstrates an enhanced performance in detection accuracy. Visual explanations for AI decisions highlight the importance of certain signal features, making the model’s operations transparent to healthcare providers. The proposed AI model addresses the crucial need for transparent and interpretable AI in healthcare. By providing real-time, explainable OSA detection, this approach represents a significant advancement in the field, potentially improving patient care and aiding in the early identification and management of OSA.
9
Sarma Borah, Proyash Paban, Devraj Kashyap, Ruhini Aktar Laskar, and Ankur Jyoti Sarmah. "A Comprehensive Study on Explainable AI Using YOLO and Post Hoc Method on Medical Diagnosis." Journal of Physics: Conference Series 2919, no. 1 (December 1, 2024): 012045. https://doi.org/10.1088/1742-6596/2919/1/012045.
Повний текст джерелаАнотація:
Abstract Medical imaging plays a pivotal role in disease detection and intervention. The black-box nature of deep learning models, such as YOLOv8, creates challenges in interpreting their decisions. This paper presents a toolset to enhance interpretability in AI based diagnostics by integrating Explainable AI (XAI) techniques with YOLOv8. This paper explores implementation of post hoc methods, including Grad-CAM and Eigen CAM, to assist end users in understanding the decision making of the model. This comprehensive evaluation utilises CT-Datasets, demonstrating the efficacy of YOLOv8 for object detection in different medical fields. This paper compares the interpretability offered by different post hoc methods, shedding light on abnormalities detected by the model. Moreover, this paper introduces a user-friendly interface for end users, incorporating the generated heat maps for intuitive understanding using different CAM algorithms. These findings underscore the importance of XAI in medical image analysis and offer a practical framework for improving interpretability in X-ray diagnostics. The comparison of the different CAM methods can offer a choice for end users to determine the best fit for deployable tools. This work contributes to bridging the gap between sophisticated deep learning models and actionable insights for professionals. Access at https://spritan.github.io/YOLOv8_Explainer/
10
Gupta, Ayush, Jeya Mala D., Vishal Kumar Yadav, and Mayank Arora. "Dissecting Retinal Disease: A Multi-Modal Deep Learning Approach with Explainable AI for Disease Classification across Various Classes." International Journal of Online and Biomedical Engineering (iJOE) 21, no. 02 (February 17, 2025): 38–51. https://doi.org/10.3991/ijoe.v21i02.51409.
Повний текст джерелаАнотація:
This study investigates the efficacy of various deep learning (DL) models in detecting retinal diseases, specifically focusing on cataract detection. Utilizing a pre-processed fundus images data set classified into normal and cataract classes, we evaluate the performance of ResNet, VGG-16 and VGG-19 models based on accuracy, sensitivity, and specificity in classifying fundus images. The primary objective of this work is to provide explanations on the predictions done by the said DL models in order to ensure the ground-truth verification. The explanation is achieved using the explainable artificial intelligence (XAI) model namely gradient-weighted class activation mapping (Grad-CAM), which helps to visualize and interpret the decision-making process of these models. Through a comprehensive exploratory data analysis (EDA), model training, and evaluation, VGG-19 emerged as the superior model, achieving the highest accuracy, precision, and recall. Grad-CAM heat maps provide insights into the models’ attention in image features, highlighting the impact of cataracts on retinal structure. The study underscores the potential of DL in retinal disease detection and the pivotal role of explainable artificial intelligence (XAI) in enhancing model interpretability. Future directions include exploring more advanced DL architectures and furthering the application of XAI techniques to improve detection systems’ accuracy and transparency.
Дисертації з теми "Interpretability of AI Models for Parkinson's Disease Detection"
1
Filali, razzouki Anas. "Deep learning-based video face-based digital markers for early detection and analysis of Parkinson disease." Electronic Thesis or Diss., Institut polytechnique de Paris, 2025. http://www.theses.fr/2025IPPAS002.
Повний текст джерелаАнотація:
Cette thèse vise à développer des biomarqueurs numériques robustes pour la détection précoce de la maladie de Parkinson (MP) en analysant des vidéos faciales afin d'identifier les changements associés à l'hypomimie. Dans ce contexte, nous introduisons de nouvelles contributions à l'état de l'art : l'une fondée sur l'apprentissage automatique superficiel et l'autre fondée sur l'apprentissage profond. La première méthode utilise des modèles d'apprentissage automatique qui exploitent des caractéristiques faciales extraites manuellement, en particulier les dérivés des unités d'action faciale (AUs). Ces modèles intègrent des mécanismes d'interprétabilité qui permettent d'expliquer leur processus de décision auprès des parties prenantes, mettant en évidence les caractéristiques faciales les plus distinctives pour la MP. Nous examinons l'influence du sexe biologique sur ces biomarqueurs numériques, les comparons aux données de neuroimagerie et aux scores cliniques, et les utilisons pour prédire la gravité de la MP. La deuxième méthode exploite l'apprentissage profond pour extraire automatiquement des caractéristiques à partir de vidéos faciales brutes et des données de flux optique en utilisant des modèles fondamentaux basés sur les Vision Transformers pour vidéos. Pour pallier le manque de données d'entraînement, nous proposons des techniques avancées d'apprentissage par transfert adaptatif, en utilisant des modèles fondamentaux entraînés sur de grands ensembles de données pour la classification de vidéos. De plus, nous intégrons des mécanismes d'interprétabilité pour établir la relation entre les caractéristiques extraites automatiquement et les AUs faciales extraites manuellement, améliorant ainsi la clarté des décisions des modèles. Enfin, nos caractéristiques faciales générées proviennent à la fois de données transversales et longitudinales, ce qui offre un avantage significatif par rapport aux travaux existants. Nous utilisons ces enregistrements pour analyser la progression de l'hypomimie au fil du temps avec ces marqueurs numériques, et sa corrélation avec la progression des scores cliniques. La combinaison des deux approches proposées permet d'obtenir une AUC (Area Under the Curve) de classification de plus de 90%, démontrant l'efficacité des modèles d'apprentissage automatique et d'apprentissage profond dans la détection de l'hypomimie chez les patients atteints de MP à un stade précoce via des vidéos faciales. Cette recherche pourrait permettre une surveillance continue de l'hypomimie en dehors des environnements hospitaliers via la télémédecineThis thesis aims to develop robust digital biomarkers for early detection of Parkinson's disease (PD) by analyzing facial videos to identify changes associated with hypomimia. In this context, we introduce new contributions to the state of the art: one based on shallow machine learning and the other on deep learning.The first method employs machine learning models that use manually extracted facial features, particularly derivatives of facial action units (AUs). These models incorporate interpretability mechanisms that explain their decision-making process for stakeholders, highlighting the most distinctive facial features for PD. We examine the influence of biological sex on these digital biomarkers, compare them against neuroimaging data and clinical scores, and use them to predict PD severity.The second method leverages deep learning to automatically extract features from raw facial videos and optical flow using foundational models based on Video Vision Transformers. To address the limited training data, we propose advanced adaptive transfer learning techniques, utilizing foundational models trained on large-scale video classification datasets. Additionally, we integrate interpretability mechanisms to clarify the relationship between automatically extracted features and manually extracted facial AUs, enhancing the comprehensibility of the model's decisions.Finally, our generated facial features are derived from both cross-sectional and longitudinal data, which provides a significant advantage over existing work. We use these recordings to analyze the progression of hypomimia over time with these digital markers, and its correlation with the progression of clinical scores.Combining these two approaches allows for a classification AUC (Area Under the Curve) of over 90%, demonstrating the efficacy of machine learning and deep learning models in detecting hypomimia in early-stage PD patients through facial videos. This research could enable continuous monitoring of hypomimia outside hospital settings via telemedicine
Частини книг з теми "Interpretability of AI Models for Parkinson's Disease Detection"
1
Mittal, Shashank, Priyank Kumar Singh, Saikat Gochhait, and Shubham Kumar. "Explainable AI (XAI) for Green AI-Powered Disease Prognosis." In Advances in Medical Diagnosis, Treatment, and Care, 141–60. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-1243-8.ch008.
Повний текст джерелаАнотація:
Accurate disease prognosis is crucial for improved healthcare outcomes. Artificial intelligence (AI) offers immense potential in this domain, but traditional “black-box” models lack interpretability. This chapter explores the integration of Explainable AI (XAI) with Green AI, a resource-efficient and sustainable approach to AI development. They discuss how XAI can enhance trust in Green AI models for disease prognosis, mitigate potential biases, and promote responsible AI development. They highlight the challenges of balancing interpretability with efficiency and propose future research directions to unlock the full potential of XAI for Green AI-powered disease prognosis. This approach has the potential to revolutionize healthcare by providing accurate, transparent, and environmentally friendly tools for early disease detection and improved patient outcomes.
2
Dehankar, Pooja, and Susanta Das. "Detection of Heart Disease Using ANN." In Future of AI in Biomedicine and Biotechnology, 182–96. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-3629-8.ch009.
Повний текст джерелаАнотація:
Heart disease remains one of the leading causes of mortality worldwide. Early detection and accurate diagnosis are crucial for effective treatment and prevention of cardiac complications. Artificial neural networks (ANNs) have emerged as powerful tools for heart disease detection, leveraging their ability to learn complex patterns from data. This chapter comprehensively reviews recent studies and developments in the application of ANNs for heart disease detection, highlighting their strengths, challenges, and future directions. The chapter also explores opportunities for the field, imagining the use of federated learning for collaborative model development, the integration of AI-driven decision support systems into standard clinical workflows, and the use of explainable AI techniques to improve model interpretability. It investigates a number of methods, such as the integration of multimodal data sources, convolutional neural networks (CNNs) for image-based diagnosis, risk prediction models, and ECG analysis.
3
Biswas, Neepa, Debarpita Santra, Bannishikha Banerjee, and Sudarsan Biswas. "Harnessing the Power of Machine Learning for Parkinson's Disease Detection." In AIoT and Smart Sensing Technologies for Smart Devices, 140–55. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-0786-1.ch008.
Повний текст джерелаАнотація:
Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting millions worldwide. Early detection of PD is crucial for effective treatment and management of the disease. Deep learning (DL) and machine learning (ML) have emerged as promising approaches for detecting PD. In this study, a comparative performance analysis is done for DL and ML applications based on speech signals. DL methods using convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and ML methods employing random forest and the XGBoost model were trained and assessed. Performance of the models are evaluated using a variety of performance metrics, including accuracy, precision, recall, and F1-score. Results showed that the XGBoost model outperformed the DL models in terms of accuracy and F1 score, while the CNN and LSTM models achieved higher precision and recall. These findings suggest that XGBoost can be a useful tool for detecting PD based on speech signals, particularly in scenarios where interpretability and computational efficiency are important.
4
Tripathi, Rati Kailash Prasad, and Shrikant Tiwari. "Unravelling the Enigma of Machine Learning Model Interpretability in Enhancing Disease Prediction." In Advances in Systems Analysis, Software Engineering, and High Performance Computing, 125–53. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-8531-6.ch007.
Повний текст джерелаАнотація:
Machine learning (ML) models have made significant strides in disease prediction, providing new avenues for early detection and intervention. These models have demonstrated remarkable capabilities in analysing vast and complex datasets to identify patterns and trends that can aid in early diagnosis and treatment. However, opacity of these models often leaves healthcare practitioners and patients in the dark about the reasoning behind their predictions, raising concerns about trust, fairness, and practical adoption of AI-based disease prediction. This review delves into the critical topic of interpretability in ML models for disease prediction, its importance, techniques to achieve it, impact on clinical decision-making, challenges, and implications in healthcare. Urgent issues and moral dilemmas pertaining to model interpretability in healthcare, areas for further research to enhance interpretability of predictive models, and applications are also highlighted. Thus, the chapter provides insights into the applicability of AI-driven models to improve healthcare decision-making and patient outcomes.
5
Krishna Pasupuleti, Murali. "AI-Driven Mutation Detection: Transforming Genomic Data into Insights for Disease Prediction." In AI in Genomic Data Analysis: Identifying Disease-Causing Mutations, 1–28. National Education Services, 2024. http://dx.doi.org/10.62311/nesx/46694.
Повний текст джерелаАнотація:
Abstract: This chapter explores how AI-driven mutation detection is revolutionizing genomic data analysis and transforming it into actionable insights for disease prediction. By leveraging advanced machine learning algorithms, such as convolutional neural networks and deep learning models, AI can accurately identify genetic mutations, including single nucleotide polymorphisms (SNPs) and structural variations, that are linked to various diseases. The chapter highlights AI's ability to process vast amounts of genomic data, uncover hidden patterns, and predict disease risk with unprecedented precision. Additionally, it examines AI's integration with bioinformatics tools, next-generation sequencing, and clinical databases to drive personalized medicine and enhance predictive capabilities in areas like cancer, cardiovascular disorders, and rare genetic diseases. Ethical considerations and challenges related to data privacy, model interpretability, and the future potential of AI in genomics are also discussed. Keywords: AI-driven mutation detection, genomic data, disease prediction, machine learning, deep learning, SNPs, structural variations, bioinformatics, next-generation sequencing, personalized medicine, predictive analytics, genetic mutations, cancer prediction, cardiovascular disorders, rare genetic diseases, ethical considerations.
6
Tafadzwa Mpofu, Kelvin, and Patience Mthunzi-Kufa. "Recent Advances in Artificial Intelligence and Machine Learning Based Biosensing Technologies." In Current Developments in Biosensor Applications and Smart Strategies [Working Title]. IntechOpen, 2025. https://doi.org/10.5772/intechopen.1009613.
Повний текст джерелаАнотація:
Advancements in artificial intelligence (AI) and machine learning (ML) have transformed biosensing technologies, enhancing data acquisition, analysis, and interpretation in biomedical diagnostics. This chapter explores AI integration into biosensing, focusing on natural language processing (NLP), large language models (LLMs), data augmentation, and various learning paradigms. These technologies improve biosensor sensitivity, precision, and real-time adaptability. NLP automates biomedical text extraction, while LLMs facilitate complex decision-making using vast datasets. Data augmentation mitigates dataset limitations, strengthening ML model training and reducing overfitting. Supervised learning drives predictive models for disease detection, whereas unsupervised learning uncovers hidden biomarker patterns. Reinforcement learning optimizes sensor operations, calibration, and autonomous control in dynamic environments. The chapter discusses case studies, emerging trends, and challenges in AI-driven biosensing. AI’s convergence with edge computing and Internet of Things (IoT)-enabled biosensors enhances real-time data processing, reducing latency and expanding accessibility in resource-limited settings. Ethical concerns, including data privacy, model interpretability, and regulatory compliance, must be addressed for responsible AI applications in biosensing. Future research should focus on developing AI models resilient to bias, capable of continuous learning, and optimized for low-power, portable biosensors. Addressing these challenges will enable AI-powered biosensing to advance precision medicine and improve global healthcare outcomes. Through interdisciplinary approaches, AI and ML will continue to drive the evolution of next-generation diagnostic solutions.
7
Sharma, Ajay, Devendra Babu Pesarlanka, and Shamneesh Sharma. "Harnessing Machine Learning and Deep Learning in Healthcare From Early Diagnosis to Personalized Treatment." In Advances in Healthcare Information Systems and Administration, 369–98. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-7277-7.ch012.
Повний текст джерелаАнотація:
Machine learning (ML) and deep learning (DL) are transforming healthcare by improving patient outcomes, reducing costs, and accelerating drug development. ML algorithms analyze large datasets such as EHRs, medical imaging, and genomics to enable early disease detection and personalized treatments. The current work highlights new approaches in pharmaceutical design and predicts medication side effects. Deep Learning (DL), a branch of AI using neural networks, excels in medical imaging, identifying subtle patterns in MRIs and X-rays. The current manuscript highlights how DL models can identify genetic markers linked to diseases like cancer, Parkinson's, and Alzheimer's. Integrating ML and DL into clinical workflows empowers healthcare professionals with data-driven tools for better decision-making. However, some challenges remain, including ensuring data privacy, and security, addressing biases in algorithms. Collaboration between healthcare providers, researchers, and tech firms is essential for the ethical and effective adoption of these technologies have been discussed in the work.
8
Raj, Sundeep, Arun Prakash Agarwal, Sandesh Tripathi, and Nidhi Gupta. "Prediction and Analysis of Digital Health Records, Geonomics, and Radiology Using Machine Learning." In Prediction in Medicine: The Impact of Machine Learning on Healthcare, 24–43. BENTHAM SCIENCE PUBLISHERS, 2024. http://dx.doi.org/10.2174/9789815305128124010005.
Повний текст джерелаАнотація:
Building different machine learning algorithms and their potential applications to enhance healthcare systems is very important. AI has countless uses in healthcare, including the analysis of medical data, early disease diagnosis and detection, evidence-based objectives to minimize human error, reducing errors between and among observers, risk identification and interventions for healthcare management, health monitoring in real-time, helping patients and clinicians choose the right medication, and assessing drug responses. Machine learning techniques have transformed many facets of healthcare, ranging from new tools that allow people to better control their health to new models that assist physicians in making more accurate decisions. Since the advent of the pacemaker and the first computerized records for blood test results and chest X-ray reports by Kaiser in the 1950s, physicians have seen the potential of algorithms to save lives. As new developments in image processing, deep learning, and natural language processing are revolutionizing the healthcare sector, this rich history of machine learning for healthcare feeds innovative research today.It is necessary to comprehend the human effects of machine learning, including transparency, justice, regulation, simplicity of deployment, and integration into clinical processes, in order to use it to enhance patient outcomes. The application of machine learning for risk assessment and diagnosis, illness progression modeling, enhancing clinical workflows, and precision medicine will be covered in this chapter, which starts with an introduction to clinical care and data. We shall include all methodological details for each of these covering topics like algorithmic fairness, causal inference, offpolicy reinforcement learning, interpretability of ML models, and the foundations of deep learning on imaging and natural language.Advances in AI and ML technologies have significantly improved the ability to forecast and recognize health emergencies, disease conditions, disease populations, and immunological responses, to name a few. Even though there is still doubt about the usefulness of ML-based techniques and how to interpret their findings in clinical contexts, their use is spreading quickly. Here, we provide a succinct introduction to machine learning-based methodologies and learning algorithms, such as reinforcement learning, supervised learning, and unsupervised learning, with examples. Subsequently, we explore the applications of machine learning (ML) in various healthcare domains such as genetics, neuroimaging, radiology, and electronic health records. Along with offering ideas for potential future uses, we also skim the surface regarding the dangers and difficulties associated with applying machine learning to the healthcare industry, including issues of privacy and ethics.