Artificial intelligence (AI) is an emerging and innovative field with the potential to change every industry from the core. AI greatly surpasses the human brain with respect to efficiency and accuracy, it utilizes available data to learn how to solve tasks. AI has displayed great potential through its successful integration in many fields, such as voice assistant, autonomous driving, and more.
The advantages of AI make its integration in healthcare and biomedicine essential and inevitable. AI enables the big data in healthcare to be disintegrated and analyzed for a greater understanding and observe patterns and risks that the human mind might fail to notice.
In healthcare, AI has been promising by analyzing genomic and biomedical data, representing drug-like molecules, and modeling cells and their functions. The success stories of AI are not limited to biomedicine research, but extend to diagnosing conditions and use in inpatient healthcare. AI technologies have outperformed accuracy of humans to detect breast cancer and predict sepsis. This has made AI into a field that calls attention due to its potential to revolutionize healthcare accuracy and understanding of biomedicine. The privacy concerns that are associated with use of data are also adhered by AI.
Of late, there have been various AI approaches proposed to preserve privacy in biomedicine. The approaches can be categorized into four groups: differential privacy, cryptographic techniques, hybrid approaches, and federated learning.
Cryptographic techniques comprise homomorphic encryptions to be performed on statistics and compute these preserving data privacy at the same time. Homographic encryptions can be partial or full and can determine the level of operations that data has experienced. Partially homomorphic encryptions implies that data has experienced either multiplication or addition operations and fully homomorphic encryptions implies that multiplication and addition operations have been applied to encrypted data.
