Federated learning is a way to train AI models that lets different healthcare groups work together without sharing patient data. Instead of sending raw data, each group keeps their data safe and only shares model updates with a central system. This helps keep patient information private and lets healthcare providers use bigger, more varied data sets to make AI more accurate.
In the US healthcare system, which has many rules and lots of data from hospitals and clinics, federated learning solves key problems about sharing data. It allows teamwork without giving out direct access to patient records, which lowers the chance of data leaks or misuse.
Still, federated learning faces problems when data is very different across organizations because of different patients, equipment, or care methods. This difference can make AI models less effective. Also, federated learning is at risk from attacks where hackers try to mess up the data during training to weaken the model.
Blockchain is a system that keeps records safe and hard to change. When used with federated learning, it helps fix some problems and makes the data and the AI model more trustworthy.
Recent studies used big sets of eye images from Singapore, China, and Taiwan to test blockchain-enhanced federated learning. The AI model was good at spotting eye diseases like myopic macular degeneration and classifying macular disease with scores showing strong accuracy.
This AI was also strong against attacks that try to confuse it by changing labels in the training data. Usually, such attacks cause problems in shared AI systems. The blockchain-based system kept up performance similar to AI models that need all the patient data collected in one place, which is hard to do in the US due to strict laws.
For US healthcare managers, these results show a way to work together on AI while keeping patient data safe and following privacy rules.
Using AI and blockchain together also helps automate parts of healthcare work, especially in offices and administration.
Hospital leaders and IT managers can use blockchain and federated learning to:
Experts say that by the end of 2025, over 60% of organizations will use AI combined with blockchain. In healthcare, 85% of leaders expect this to improve decisions in both medical and administrative tasks. The US healthcare system will benefit from ongoing research aimed at better scaling, privacy, and easier integration with current hospital technology.
New ideas like using tokens for AI models may create ways for developers to safely earn money from their AI inside health networks. Also, real-time AI-blockchain systems might become a key part of protecting hospitals from cyber threats.
Blockchain combined with federated learning offers a way for US healthcare providers to build strong AI systems together while keeping patient data safe and trusted. As healthcare grows more digital and privacy rules get stricter, this method fits well with operational and legal needs. By joining AI’s data skills with blockchain’s security, medical leaders can improve healthcare services and office tasks while protecting important patient information.
Federated Learning is a privacy-preserving technology that enables collaboration among healthcare institutions to develop AI models without transferring raw patient data. It allows for decentralized model training while maintaining data privacy.
FL faces challenges such as non-independent and identically distributed (non-IID) data typical in healthcare settings, which can lead to reduced model performance and susceptibility to privacy breaches.
Integrating blockchain with FL enhances security by providing a trustworthy method for transferring model updates among collaborative sites, ensuring the integrity and provenance of shared model parameters.
The study employed a retrospective multicohort analysis using 27,145 retinal images to evaluate the FL model’s performance in detecting myopic macular degeneration and classifying OCT images under various conditions.
The FL model achieved high performance metrics with an AUC of 0.868 for MMD detection and 0.970 for OCT classification, demonstrating robustness even under adversarial attack scenarios.
Adversarial attacks, such as label flipping and clean label attacks, aim to manipulate model outcomes. The study found that the FL model demonstrated resilience against these attacks compared to other models.
The incorporation of blockchain into the FL framework added minimal time to the model development process, approximately 5 additional seconds per global epoch.
Non-IID situations refer to the variability in data distribution across different healthcare institutions, impacting the performance of FL algorithms due to differences in feature and label distributions.
Blockchain-enabled FL can form a trusted platform for collaborative healthcare AI research, optimizing data analysis without compromising patient privacy or data security.
Future research should focus on enhancing FL frameworks to manage non-IID data more effectively and improve defenses against adversarial attacks while exploring additional applications across healthcare domains.
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