In the realm of healthcare, artificial intelligence (AI) has shown remarkable potential during the development phase. However, once these technologies are deployed in real-world scenarios, their effectiveness can diminish significantly.
One of the primary issues is the presence of coding bias, which can skew results and lead to unequal healthcare outcomes for different populations. This bias often goes unnoticed during the controlled testing phases.
To ensure that AI can truly benefit all patients, it is essential to understand its limitations and address the biases inherent in its coding. Only then can we hope to achieve equitable healthcare solutions.