Addressing bias and Norwegian adaptation
One of the biggest challenges with implementing AI is the risk of bias in the models developed. Bias in AI systems is not just a technical issue but also an ethical and clinical problem that can have direct consequences for patient care. An example of this is race-based adjustments in pulmonary function tests (PFTs), where it has long been standard practice to apply different 'normal' values for lung function based on the patient's race. This has raised concerns that such adjustments may underestimate the severity of lung diseases in Black patients, potentially leading to delayed diagnosis and treatment (4).
In Norway, we must be particularly aware of how bias can affect the treatment of vulnerable groups, such as the Sami population, immigrants and patients from various socioeconomic backgrounds. Bias in AI models could reinforce existing health disparities if the dataset used to train the models does not represent the entire population. For example, if an AI model is developed based on data from a population in which certain groups are underrepresented, the results may be skewed, leading to poorer treatment for these groups.
To counteract bias and ensure fair AI implementation in Norwegian healthcare, we propose several measures.
First, we must ensure representative data collection. Datasets used to train AI models must include all population groups in Norway, including ethnic minorities, different age groups, genders and socioeconomic backgrounds. Specific data collection initiatives should be launched to include underrepresented groups.
Second, regular review is necessary. AI models must be regularly reviewed to identify and correct biases. This means establishing a national system to monitor and evaluate the performance of AI models in clinical practice.
In terms of ethical considerations, interdisciplinary panels comprising clinicians, ethicists, patient representatives and AI experts should assess potential biases and ethical implications before implementing the models.
Moreover, AI models developed outside Norway should be evaluated and adapted to Norwegian conditions before use. This involves not only linguistic translation but also adjustments to Norwegian clinical guidelines, healthcare priorities, and cultural norms.
Lastly, healthcare professionals and patients should be informed about the limitations of AI models, including potential biases, so that they can make informed decisions.
By implementing these measures, we can develop an AI system that is fair and robust, enhancing both the quality of healthcare services and trust in AI among patients and healthcare professionals alike.