Federated Learning Systems for Healthcare: Perspective and Recent Progress

Kumar, Yogesh; Singla, Ruchi

doi:10.1007/978-3-030-70604-3_6

Cited by 48 publications

(19 citation statements)

References 14 publications

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“…Prompt action based on accurate data has a significant social and financial impact on the lives of people around the world [ 51 ]. The use of AI in healthcare has improved the collection and processing of valuable data and, at higher levels, the programming of surgical robots [ 54 ]. AI describes a machine's power to study how a human learns by image recognition and pattern recognition in a problematic situation.…”

Section: Artificial Intelligence Techniques In Disease Diagnosis and ...mentioning

confidence: 99%

Evaluation of artificial intelligence techniques in disease diagnosis and prediction

Nia

Kaplanoğlu

Nasab

2023

Discov Artif Intell

117

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A broad range of medical diagnoses is based on analyzing disease images obtained through high-tech digital devices. The application of artificial intelligence (AI) in the assessment of medical images has led to accurate evaluations being performed automatically, which in turn has reduced the workload of physicians, decreased errors and times in diagnosis, and improved performance in the prediction and detection of various diseases. AI techniques based on medical image processing are an essential area of research that uses advanced computer algorithms for prediction, diagnosis, and treatment planning, leading to a remarkable impact on decision-making procedures. Machine Learning (ML) and Deep Learning (DL) as advanced AI techniques are two main subfields applied in the healthcare system to diagnose diseases, discover medication, and identify patient risk factors. The advancement of electronic medical records and big data technologies in recent years has accompanied the success of ML and DL algorithms. ML includes neural networks and fuzzy logic algorithms with various applications in automating forecasting and diagnosis processes. DL algorithm is an ML technique that does not rely on expert feature extraction, unlike classical neural network algorithms. DL algorithms with high-performance calculations give promising results in medical image analysis, such as fusion, segmentation, recording, and classification. Support Vector Machine (SVM) as an ML method and Convolutional Neural Network (CNN) as a DL method is usually the most widely used techniques for analyzing and diagnosing diseases. This review study aims to cover recent AI techniques in diagnosing and predicting numerous diseases such as cancers, heart, lung, skin, genetic, and neural disorders, which perform more precisely compared to specialists without human error. Also, AI's existing challenges and limitations in the medical area are discussed and highlighted.

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Section: Artificial Intelligence Techniques In Disease Diagnosis and ...mentioning

confidence: 99%

Evaluation of artificial intelligence techniques in disease diagnosis and prediction

Nia

Kaplanoğlu

Nasab

2023

Discov Artif Intell

117

View full text Add to dashboard Cite

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“…The best thing about applying AI in health care is to improve from gathering and processing valuable data to programming surgeon robots. This section expounds on the various techniques and applications of artificial intelligence, disease symptoms, diagnostics issues, and a framework for disease detection modelling using learning models and AI in healthcare applications (Kumar and Singla 2021).…”

Section: Artificial Intelligence In Disease Diagnosismentioning

confidence: 99%

Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

Kumar

Koul

Singla³

et al. 2022

J Ambient Intell Human Comput

Self Cite

483

173

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Artificial intelligence can assist providers in a variety of patient care and intelligent health systems. Artificial intelligence techniques ranging from machine learning to deep learning are prevalent in healthcare for disease diagnosis, drug discovery, and patient risk identification. Numerous medical data sources are required to perfectly diagnose diseases using artificial intelligence techniques, such as ultrasound, magnetic resonance imaging, mammography, genomics, computed tomography scan, etc. Furthermore, artificial intelligence primarily enhanced the infirmary experience and sped up preparing patients to continue their rehabilitation at home. This article covers the comprehensive survey based on artificial intelligence techniques to diagnose numerous diseases such as Alzheimer, cancer, diabetes, chronic heart disease, tuberculosis, stroke and cerebrovascular, hypertension, skin, and liver disease. We conducted an extensive survey including the used medical imaging dataset and their feature extraction and classification process for predictions. Preferred reporting items for systematic reviews and Meta-Analysis guidelines are used to select the articles published up to October 2020 on the Web of Science, Scopus, Google Scholar, PubMed, Excerpta Medical Database, and Psychology Information for early prediction of distinct kinds of diseases using artificial intelligence-based techniques. Based on the study of different articles on disease diagnosis, the results are also compared using various quality parameters such as prediction rate, accuracy, sensitivity, specificity, the area under curve precision, recall, and F1-score.

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“…The increasing interest in this field assisted in having several frameworks or platforms that implement FL. Some of those frameworks are [ 65 , 92 , 93 ]: Tensorflow federated (TFF) algorithm [ 94 ]: an open source framework for experimenting with FL that enables developers to experiment with novel FL algorithms as well as simulating existing ones on their data; Federated AI technology enabler (FATE) [ 95 ]: relies on homomorphic encryption and supports a range of FL architectures and secure computation algorithms including logistic regression, tree-based algorithms, neural networks and transfer learning; PySyft [ 96 ]: developed by OpenMined and decouples private data from model training using federated learning, differential privacy and multiparty computation; Tensor/IO [ 97 ]: a lightweight cross-platform library for on-device machine learning, bringing the power of TensorFlow and TensorFlow Lite to iOS, Android, and React native applications; Tensorflow encrypted: provides an interface similar to that of TensorFlow and aims to make the technology readily available without requiring the user to be an expert in ML, cryptography, distributed systems, and high-performance computing; CoMind: built on top of TensorFlow and provides high-level APIs for implementing FL and FedAvg specifically; Horovod: based on the open message passing interface (MPI) and works on top of popular deep learning frameworks, such as TensorFlow and PyTorch; LEAF benchmark: is a modular benchmarking framework for machine learning in federated settings, with applications in FL, multi-task learning, meta-learning, and on-device learning aiming to capture the reality, obstacles, and intricacies of practical FL environments. …”

Section: Federated Learningmentioning

confidence: 99%

“…In addition, federated learning has the potential to play an important role in healthcare by enabling the training of models using distributed and decentralized health data [ 51 , 93 , 98 ]. This can help protect patient privacy while enabling the creation of more accurate and personalized models and the analysis of more data, as long as privacy is maintained.…”

Section: Federated Learning In Actionmentioning

confidence: 99%

“…In addition, FL has increased its efficiency in learning from data that are distributed across multiple sites and cannot be combined into a single dataset, or when data reside in multiple clinical systems [ 101 ]. In summary, FL can significantly improve the quality of healthcare by making it more data-driven and personalized [ 93 , 98 , 101 ].…”

Section: Federated Learning In Actionmentioning

confidence: 99%

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Reviewing Federated Machine Learning and Its Use in Diseases Prediction

Moshawrab

Adda

Bouzouane

et al. 2023

Sensors

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Machine learning (ML) has succeeded in improving our daily routines by enabling automation and improved decision making in a variety of industries such as healthcare, finance, and transportation, resulting in increased efficiency and production. However, the development and widespread use of this technology has been significantly hampered by concerns about data privacy, confidentiality, and sensitivity, particularly in healthcare and finance. The “data hunger” of ML describes how additional data can increase performance and accuracy, which is why this question arises. Federated learning (FL) has emerged as a technology that helps solve the privacy problem by eliminating the need to send data to a primary server and collect it where it is processed and the model is trained. To maintain privacy and improve model performance, FL shares parameters rather than data during training, in contrast to the typical ML practice of sending user data during model development. Although FL is still in its infancy, there are already applications in various industries such as healthcare, finance, transportation, and others. In addition, 32% of companies have implemented or plan to implement federated learning in the next 12–24 months, according to the latest figures from KPMG, which forecasts an increase in investment in this area from USD 107 million in 2020 to USD 538 million in 2025. In this context, this article reviews federated learning, describes it technically, differentiates it from other technologies, and discusses current FL aggregation algorithms. It also discusses the use of FL in the diagnosis of cardiovascular disease, diabetes, and cancer. Finally, the problems hindering progress in this area and future strategies to overcome these limitations are discussed in detail.

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Federated Learning Systems for Healthcare: Perspective and Recent Progress

Cited by 48 publications

References 14 publications

Evaluation of artificial intelligence techniques in disease diagnosis and prediction

Evaluation of artificial intelligence techniques in disease diagnosis and prediction

Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

Reviewing Federated Machine Learning and Its Use in Diseases Prediction

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