Khalid Nazish, Qayyum Adnan, Bilal Muhammad, Al-Fuqaha Ala, Qadir Junaid
Information Technology University, Lahore, Pakistan.
Big Data Enterprise and Artificial Intelligence Lab (Big-DEAL), University of the West England, Bristol, United Kingdom.
Comput Biol Med. 2023 May;158:106848. doi: 10.1016/j.compbiomed.2023.106848. Epub 2023 Apr 5.
There has been an increasing interest in translating artificial intelligence (AI) research into clinically-validated applications to improve the performance, capacity, and efficacy of healthcare services. Despite substantial research worldwide, very few AI-based applications have successfully made it to clinics. Key barriers to the widespread adoption of clinically validated AI applications include non-standardized medical records, limited availability of curated datasets, and stringent legal/ethical requirements to preserve patients' privacy. Therefore, there is a pressing need to improvise new data-sharing methods in the age of AI that preserve patient privacy while developing AI-based healthcare applications. In the literature, significant attention has been devoted to developing privacy-preserving techniques and overcoming the issues hampering AI adoption in an actual clinical environment. To this end, this study summarizes the state-of-the-art approaches for preserving privacy in AI-based healthcare applications. Prominent privacy-preserving techniques such as Federated Learning and Hybrid Techniques are elaborated along with potential privacy attacks, security challenges, and future directions.
将人工智能（AI）研究转化为经过临床验证的应用程序，以提高医疗服务的性能、能力和功效，这方面的兴趣与日俱增。尽管全球进行了大量研究，但很少有基于AI的应用程序成功进入临床。广泛采用经过临床验证的AI应用程序的主要障碍包括医疗记录不标准化、精选数据集的可用性有限，以及保护患者隐私的严格法律/道德要求。因此，迫切需要在AI时代改进新的数据共享方法，以便在开发基于AI的医疗应用程序时保护患者隐私。在文献中，人们已将大量注意力投入到开发隐私保护技术以及克服阻碍AI在实际临床环境中应用的问题上。为此，本研究总结了在基于AI的医疗应用程序中保护隐私的最新方法。详细阐述了诸如联邦学习和混合技术等著名的隐私保护技术，以及潜在的隐私攻击、安全挑战和未来发展方向。
