Responsible AI in Healthcare Industry | The Ultimate Guide

Introduction to Responsible AI

Experts have raised concerns about AI ethics and transparency, with the rising failure of artificial intelligence applications. Currently, the black box implementation cannot detect issues. It is a crucial industry, so these ethical challenges must be identified and mitigated since artificial intelligence can threaten patient privacy, safety, and preferences. Such issues hinder its adoption in the healthcare industry. The issue must be resolved because any action or decision taken in the industry is a matter of life and death for a person. Several questions are raised before the adoption of artificial intelligence in healthcare:

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• In the industry, it is a matter of life and death and the well-being of individuals, so to what extent can we rely on artificial intelligence algorithms?

• How do we safeguard that its algorithms are only used for intended purposes?

• How do we ensure that AI does not discriminate against specific cultures, communities, or other groups?

Responsible AI in healthcare

Although it creates business value and benefits, it also raises a host of unwanted and severe consequences, such as privacy violations, discrimination, inequalities, proliferation, etc.

Artificial Intelligence applications require careful management to prevent unintentional but significant damage to society. Justifying how computers reach particular solutions can solve this issue and build confidence between humans and computers.

What are the common challenges? 

The complexity of algorithms increases to deliver better outcomes. Thus, understanding these algorithms is also becoming challenging due to their complexity. It raises reliability issues in healthcare. This process can’t work for the industry because knowing how and why artificial intelligence gives these results is very important. Below are listed some of the challenges of AI in healthcare:

Diagnostic Error

• Diagnostic errors account for 60% of all healthcare errors. This error could result in a large number of wrong diagnostic approaches.

• Undoubtedly, it can offer more accurate diagnostics, but there is always a chance of making mistakes. Thus, companies hesitate to adopt artificial intelligence in diagnosis.

• The primary reason for these errors is poor quality and biased data.

Quality Data Collection

ML model needs fair and reasonable data quality to make accurate predictions, but finding high-quality, bias-free data is a significant challenge in the healthcare industry. Biased data might perpetuate inequalities.

Privacy and Security

Data privacy and security are prominent issues. As patient data may contain some sensitive details, such as personally identifiable information, according to the Health Insurance Portability and Accountability Act (HIPA) and GDPR (General Data Protection Regulation), this type of data must be protected. Concerns about data privacy, security, and leakage reduce the adoption of artificial intelligence. For instance, the University of Washington accidentally shared data with almost 1 million people due to database configuration errors.

Data ingestion, use, and preprocessing have become increasingly difficult as more unstructured data is ingested from various sources. Hence, it’s easy to fall prey to pitfalls such as inadvertently using or revealing sensitive information hidden among anonymised data.

What are the approaches for Responsible AI in healthcare?

The best approaches are described in the following paragraphs:

Empower leadership

A chief artificial intelligence ethics officer should be appointed as a leader. This officer will be responsible for convening stakeholders, identifying champions across the organisation, and establishing principles. They also guide the team in the creation of AI systems.

Develop principles, policies, and training

Responsible artificial intelligence principles are the foundation of it. They are an integral part of it. It is very important to spend time on principles, policies, and training. The team should have a clear idea of principles.

Human + AI governance

Defining an ethical framework, roles, responsibilities, procedures, and policies are equally important to building a successful, Responsible artificial intelligence application. It should have a highly effective governing system to review things and bridge gaps.

Conduct Reviews

A process should be built to conduct an end-to-end review. Effective integration depends on regularly assessing the risks and biases associated with each use case's outcomes. Its practitioners can identify risks early and flag them to resolve them immediately.

Integrate tools and methods

Evolving and integrating tools is very important for a productive and impactful system. Manually conducting a conductive review is time-consuming and complex. However, if there is a tool to perform that task, it could simplify the process.

Build and test a response plan

In addition to the Responsible AI procedure, there should be a proper plan to handle any lapse and a well-tested plan to handle it. Policies and procedures must be developed, validated, tested, and refined to ensure that harmful consequences are minimised to the greatest extent possible if an artificial intelligence system fails.

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What are its benefits in the industry?

The benefits of responsible artificial intelligence are described below:

Minimise unintended bias

It helps mitigate the bias in data and algorithms to ensure and build bias-free and responsible AI applications.

AI Transparency

Explainable artificial intelligence interprets the workings of an algorithm, helping us understand how an algorithm makes decisions.

Ensure Privacy

Protect the privacy and security of data using Privacy-Preservation AI. It keeps sensitive information secure from malicious attacks and ensures that sensitive data is never used unethically.

AI can accelerate medical research, prevention, diagnosis, and treatment. But humans must govern it to mitigate bias and embed explainability. This will make artificial intelligence trustworthy and responsible for its actions. The Healthcare Industry is already using AI and ML in powerful ways, but they must be applied judiciously.

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