Practicing Responsible AI in Healthcare

Practicing Responsible AI in Healthcare

Healthcare Industry and the Need for Responsible AI

The healthcare industry has always been under scrutiny, due to the magnitude of responsibility it holds, as health is one of the most significant pillars of any nation from a geopolitical standpoint as well.

The use of AI in the healthcare industry is evolving for various reasons. The primary one is to automate the maintenance of electronic health records for patients to improve doctor-patient collaboration and help prevent burnout faced by healthcare workers while maintaining records. Democratizing healthcare is another space where AI is becoming popular. Other areas are to adjust and optimize appointment scheduling for patients, waste management, and data management to meet Environmental, Social, and Governance (ESG) goals to align with sustainable practices, patient care, treatment, and disease diagnosis.

From the perspective of diseases and treatments across various disciplines in medicine, AI can help enhance treatment plans to improve outcomes and prioritize urgent cases. (1) It can also help increase accuracy in diagnosis by analyzing large datasets, including case histories. Additional innovation includes robotics systems performing surgeries. (2)

However, it’s essential to ensure that these advancements align with the principles of Responsible AI. This means developing and deploying AI systems that are ethical, transparent, and accountable. By prioritizing Responsible AI, the healthcare industry can safeguard patient privacy, prevent biases in decision-making, and ensure that AI technologies are used to enhance human well-being. This will not only have a positive impact on the medical as well as the pharmaceutical world for treatments and clinical trials.

What is Responsible AI?

Responsible AI is an approach to developing, assessing, and deploying AI systems in a way that is safe, trustworthy, and ethical. Fairness is required in ensuring AI systems treat all individuals and groups fairly, avoiding biases that could lead to discrimination due to gender biases or biases by population or ethnicity. AI systems should operate reliably and safely, even under unexpected conditions. The privacy and security of data generated by AI systems need to be protected. The design of AI systems needs to be accessible and beneficial to all users, including those with disabilities. Making AI systems understandable and their decision-making processes clear to users.

How Can We Add Responsible AI to Healthcare?

AI should be used as a collaborator, instead of a singular entity. It should be utilized to fulfill social, functional, and organizational responsibilities to support medical professionals and patients. One of the topics of focus in Davos 2025 was centered around building more equitable health systems. To design more equitable health systems that cater to everyone equally—men, women, children, healthy, and disabled—several key factors must be considered.

1. Data Quality and Diversity: Data should be representative, good quality, and diverse. This ensures that AI models are trained on a wide range of scenarios and populations, reducing biases and improving accuracy.

2. Historical Context: The NIH funded the first clinical trials in 1993, marking a significant milestone in the pursuit of evidence-based healthcare. This historical context underscores the importance of rigorous testing and validation in developing AI solutions.

3. Synthetic Data: Synthetic data can be used to compensate for the biased nature of existing data to avoid being trained on incomplete datasets while ensuring the quality and validity of the data. This can prevent incorrect results because of “hallucination” by AI tools due to incorrect or unavailable data. By generating artificial data that mimics real-world scenarios, researchers can address gaps and biases in the original datasets.

4. Building AI Literacy: Building AI literacy is a good start. Educating healthcare professionals and stakeholders about AI’s capabilities and limitations is crucial for its responsible implementation.

5. Access and Facilities: You need to shape those facilities. When you have the facilities of care, you need access. Ensuring that healthcare facilities are accessible to all is a fundamental step in creating equitable health systems. Access maps to analyze the population coverage for healthcare facilities across regions.

How to Avoid Biases in Healthcare Datasets?

The data collected should represent a wide range of demographics, including different races, genders, ages, and socioeconomic backgrounds. This helps in creating more equitable healthcare systems. Continuously audit and monitor datasets and algorithms for biases. This involves checking for any disparities in the data and the outcomes produced by the AI models. Lastly, every organization must carry out the Implicit Association Test (IAT) (3) to measure the amount of bias in their datasets and take more inclusive steps, contributing to a better future for AI in healthcare.

With awareness to incorporate Responsible AI in their leadership decisions, organizations of all sizes can make a difference.

About the author

This article was written by Yoshita Sharma.

References:

1. Dr. Mina Makary, HealthManagement.org. (2021). Artificial intelligence in radiology: Current applications and future technologies. HealthManagement, 21(4). Retrieved from HealthManagement

2. Kimia Kazemzadeh, Meisam Akhlaghdoust, Alireza Zali. Frontiers in Surgery. (2023). Retrieved from Frontiers in Surgery

3. Maina, I. W., Belton, T. D., Ginzberg, S., Singh, A., & Johnson, T. J. (2017). A decade of studying implicit racial/ethnic bias in healthcare providers using the implicit association test. Social Science & Medicine, 199,219-229. Retrieved from ScienceDirect