Artificial Intelligence Governance: Building Trust and Transparency in Healthcare AI

Healthcare is a leading sector that attracts AI investment, bringing drug research and development beyond chemical analytics, data analysis and improved clinical trial recruitment. Despite increased investment in AI, many healthcare, pharmaceutical, biotechnology and life science organizations find it difficult to put their AI use cases into production.

The inefficient and dispersed process creates a mature environment for failure. These operational breakdowns also make it difficult to recognize how building an AI governance framework can help companies keep up with innovation, understand plans, and reduce risks.

Even if your organization can produce viable AI use cases, it takes time to put the generated AI project into production, which will translate into costs – not to mention decentralized systems and processes, which are many of the challenges of AI governance adoption.

It seems that at that time – Time is money – Governance is inappropriate. For any company, proving ROI remains the top priority of the initiative. The constant pressure to generate value and competitive advantage leads to fallacies Skipping AI governance will save time and speed improvements. But establishing AI governance can ensure that AI technology is safe, ethical and effectively implemented.

This is why consistent, easy-to-understand documentation provides leaders with critical information for better transparency and improved decision making. Consider nutrition labels. Yes, you need to know quickly about the ones behind each box of cereals and fries before pouring fat, sugar or sodium into the cart or putting it back on the shelf. Nutritional labels make decisions easy. AI initiatives should be able to consistently provide the same level of transparency.

Similar to nutrition labels, the applied model card provides decision makers with an overview of AI plans, expected uses, bias, risk, warning, metadata, security and maintenance data, as well as metrics of performance, equity, security and reliability. Success means disconnecting from solution owners and developers, executives and compliance, as well as end users, and end users like patients and healthcare professionals:

• Better transparency (e.g., patients and care providers get more information about how AI affects diagnosis, treatment advice, or resource allocation)
• Higher trust (e.g., recorded risk of bias, expected use, limitations
• Improve safety (e.g., help ensure that AI tools meet clinical standards before deployment, reducing the risk of misdiagnosis or improper treatment)
• Faster innovations (e.g., standardized documentation simplifies regulatory approvals and clinical validation, which means patients can access new (but safer) AI-driven innovations faster)

The Alliance for Health AI (CHAI), which aims to advance the responsible development and supervision of AI in healthcare, says model cards (nutritional labels for AI) are a key component of the entire AI life cycle, not just the production phase. It can help ensure consistency, integrity and clarity around the proposed AI solution during the air inlet, the expected usage of files, data usage, metadata and development risks, update changes made during review and verification, publishing and sharing of data to provide consistent and easy-to-understand model information, and even updates during the monitoring and auditing phases.

Having a large number of manual processes creates risks and inefficiencies, and makes standardized approaches to AI governance difficult. Lack of standardized automation will inevitably slow down compliance efforts later in your process, ultimately delaying your AI plan to the market. On the other hand, manipulating your AI governance by automating documentation will increase transparency and collaboration in the department, which in turn will accelerate the speed and scale of innovation so you can see the return on investment sooner. Given that up to 80% of businesses have 50 generated AI use cases in the pipeline, but only a few companies put it into production, it is clear that there is room for improvement. Faster deployment is possible, but it starts with better governance, simplifying intake, clarifying ownership, standardizing and automating documents to see higher efficiency, and then expanding these practices to reduce risk and accelerate adoption of AI initiatives.

Those who recognize that AI governance is an innovation enabler will be able to align business priorities and AI investments and make smarter decisions about which model to accelerate and retired models, ultimately enabling businesses to better position their ROI on their AI. Earlier healthcare organizations realized that AI governance is a catalyst for innovation, trust and transparency, and the sooner they will achieve process efficiency and market speed, thus creating a competitive advantage.

Image source: Mr. Cole_photographer, Getty Images

This article passed Mixed Influencer Programs. Anyone can post opinions on MedCity News’ healthcare business and innovation through MedCity Remacence. Click here to learn how.