The 9 Principles of Ethical AI in Healthcare Industry
It is noticed that AI systems are not neutral and not providing valid outcomes. AI in Healthcare can raise ethical issues and can harm patients by not giving intended outcomes. Therefore it is necessary to use Ethical AI in the health industry. Akira AI provides Ethical AI systems that are taking care of ethical issues and values.
The 9 principles that are responsible for ethical AI in healthcare and provide a framework to help technologists while designing, developing, or maintaining systems. Akira AI obeys all the principles of Ethical AI; these are:
1. Social Well being 2. Avoid Unfair Bias 3.Privacy and Security 4.Reliable and Safe 5.Transparency and Explainability 6.Governable 7.Value alignment 8.Accountability 9.Human-centered
Let us know them one by one in detail!
Our AI system is available for the individual, society, and the environment. With the increasing population, the demand for AI in healthcare is increasing. The need for systems of Ethical AI in Healthcare is also increasing due to the shortage of health professionals. We provide systems “Medical imaging and diagnosis assistance” that can predict disease or anomalies in healthcare.
Sometimes it may be possible that anomalies are tiny, and the human eye cannot recognize them. Here, AI systems can help recognize those and help the doctors and patients recognize the disease and take action before things got worse. We can build AI applications for healthcare, making it difficult for humans to find patterns and make decisions.
The AI system that is designed should be ethically fair. Systems of Ethical AI in Healthcare should not do any unfair discrimination against individuals or groups. Unfair Bias provides equitable access and treatment. It detects and reduces unfair biases based on race, gender, nationality, etc. The main reason behind the bias is that algorithms are developed and trained only on a certain portion of the population, but there is diversity in the world in actuality. Thus when the same system is applied to the globe, it shows bias.
SCAD (Spontaneous Coronary Artery Dissection) is a condition when artery walls tear apart without any warning. 80% of the SCAD cases are in women, yet women are underrepresented during clinical trials. If we use this data to train and develop an AI system, the system would not understand the complexities of disease in women because it used predominantly male data. So it will move with the bias that is already present there.
Similarly, when we design a system and train it with specific geographical places and then apply it to other geographical places by revalidating it, the system can fail. To better understand, take an example, cardiovascular conditions affect European ancestry ten years earlier than India. So a system for detecting cardiovascular conditions trained with data of European people can fail when applied to Indians.
To prevent this bias, we must use data that represent diversity in the targeted users. And when the system should be used for different targeted groups, it should retrain and revalidate it.
AI systems in healthcare keep data privacy at the top.
