Ethical AI in Healthcare and its Principles

Introducing 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.


Principles of Ethical AI in Healthcare

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!


Social Well being

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.

Avoid Unfair Bias

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.


"Recently report submitted by the Canadian Medical Association state that they would trust the AI-derived diagnosis report if delivered by a physician" - Forbes


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.

Privacy and Security

AI systems in healthcare keep data privacy at the top. Our systems of ethical AI in Healthcare are designed to provide proper data governance and model management system. While designing systems, privacy, and security of the system is a top concern. Privacy and preserving AI principles helps to keep the data secure.

Reliable and Safe

The AI system should work only for the intended purpose. Our systems of ethical AI in Healthcare work appropriately. Safety measures are on priority for us. We always apply strong safety and security practices in our system. Our systems are thoroughly tested and monitored.

For example, a system is used to screen lung cancer using low dose CT scan. It is obvious to extend its use for diagnosis when the system becomes successful and well trained. To make a successful and effective system for diagnosing, they have to develop and evaluate models differently. Because the previous model is for the people who already have the disease and our new system, our target group contained those who don't have lung cancer. The system can work correctly when it is tuned differently.

Transparency and Explainability

Our system explains each prediction and output. It provides clarity for the logic of the model. Users get to know the contribution of data for the output. This disclosure justifies the output and builds trust.

Akira AI systems obey the Principles of Explainable AI; therefore, it provides complete transparency and explainability of systems that build users' trust.

Governable

We are designing a system that works on intended tasks. It detects and avoids unintended consequences. In healthcare, it is very important to reduce those unattended consequences because it can cost the patient's life. So, ethical AI in Healthcare avoids unattended consequences to save the patient's life.


"Gartner predicts that 75% of Healthcare Delivery Organizations (HDOs) will have invested in an AI capability to improve operational or clinical performance" - Gartner Inc.


Value alignment

Humans are making decisions by considering universal values. Our motive is to provide AI that also considers those universal values.

For example, there is an AI system in healthcare to interact with patients and solve the patient's queries. Or a system that is used to interact with patients to entertain themselves. That system is improving and learning from the message that it gets from the patients. But what would happen if it starts using offensive talk. This can hurt patients' sentiments and can hurt them and make their condition worse. Therefore the system must value human rights and morals.

Accountability

Our system provides opportunities for feedback and appeal. Our system is under the control of the appropriate human direction. Suppose the system is not under human control. The evaluation and maintenance of the system become complicated.

Human-centered

The system of ethical AI in healthcare values human diversity, freedom, autonomy, and rights. Also, it serves humans by respecting human values. The system is not performing any unfair and unjustified actions. It respects individual freedom and autonomy. Akira AI systems are fair and protected. Our system respects the rights of individuals.


Conclusion

Akira AI provides systems that work so that it responds to a situation in an ethical way. Systems of Ethical AI in healthcare are designed to fit in the ethical environment of society. Ethical AI helps us to make responsible AI systems.