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Using ChatGPT in Life Sciences: Why Governance is the Key

12 Apr 2023

Generative AI, and arguably its most popular avatar today - ChatGPT, has the potential to revolutionize the life sciences and healthcare industries by automating tasks, providing personalized care, and assisting with research. But as with all new technologies and experiments, the risk of untrodden paths remains.

Two key factors life sciences leaders must consider before jumping onto the ChatGPT bandwagon are: Data regulations and IP rights of derivative assets.

Many countries are putting in place more stringent regulations around data - protection and privacy, residency, use, and more. It needs to be seen how countries approach the Generative AI paradigms, so early movers need to factor in an element of caution as well as agility in their ChatGPT rollouts.

And then the question of who owns data, and what’s the final word on IP rights of derivative assets, is a key factor to consider before you dive all in. We already hear a growing number of IP infringement concerns about AI-generated content, as the lines between original and derivative work continue to blur.

So, while new technologies are always welcome, doing it in a prudent way is the need of the hour. Here are some factors life sciences leaders must consider before making the ChatGPT leap in a significant way.

1. Data Privacy and Security

Healthcare data is often sensitive and subject to strict privacy regulations, such as HIPAA in the United States. When it comes to AI policies, the General Data Protection Regulation (GDPR) is often mentioned due to its significant impact on regulating the data market, which is a crucial aspect of AI applications. The GDPR and AI intersection raises important policy issues since chatbots can process personal data. The controller, who decides on the purposes and means of processing, is primarily responsible for ensuring that personal data is processed in compliance with the GDPR. The EU AI Act will regulate the marketing and use of AI in Europe, addressing the associated risks and providing specific obligations for providers and users of AI systems like ChatGPT.

Using generative AI in the healthcare sector requires ensuring that data is protected and handled according to these regulations. To overcome this challenge, organizations must develop robust data protection policies and invest in secure AI infrastructure. Additionally, the collaboration between AI developers and healthcare providers is essential to ensure that AI systems comply with privacy regulations and maintain patient confidentiality.

2. Quality Control and Validation

Generative AI models must be rigorously tested and validated to ensure the accuracy and reliability of their outputs. This is particularly crucial in healthcare, where incorrect information can have severe consequences.

To address this challenge, organizations should establish quality control processes and validation protocols for AI-generated content. Regular monitoring and fine-tuning of AI models will ensure they continue to provide accurate and reliable information.

3. Establishing Trust

Healthcare professionals and patients may be skeptical of AI-generated advice and content. Building trust in AI systems is essential for their successful implementation in the life sciences and healthcare sectors. By Its nature, Healthcare Industry cannot accept AI, when there Is a probability and correspondingly a massive risk that the AI model may hallucinate and may not be trustworthy.

To build trust, organizations should focus on transparency, education, and communication. Providing clear explanations of how AI systems work and their limitations can help dispel misconceptions and foster trust. Additionally, organizations should involve healthcare professionals and patients in the development and evaluation of AI applications, ensuring that their perspectives and concerns are considered.

4. Domain Expertise

Generative AI systems require domain expertise to understand and contextualize information accurately. In the life sciences and healthcare sectors, this means AI models must be trained on vast amounts of relevant data and incorporate the insights of domain experts.

To overcome this challenge, organizations should collaborate with healthcare professionals, researchers, and other domain experts to develop and refine AI models. These experts can help identify relevant data sources, establish appropriate context, and ensure that AI systems produce accurate and meaningful outputs.

5. Regulatory Compliance

The use of AI in healthcare may be subject to regulatory oversight, which can present challenges for organizations looking to implement generative AI solutions. Navigating the complex landscape of healthcare regulations requires a thorough understanding of applicable laws and guidelines.

To address this challenge, organizations should engage with regulatory bodies and legal experts to ensure that AI applications comply with relevant regulations. Developing a clear understanding of the regulatory environment will help organizations avoid potential legal and financial risks associated with noncompliance.

6. Ethical Considerations

Using generative AI in healthcare raises several ethical questions, such as the potential for algorithmic bias, the implications of AI-driven decision-making, and the responsibility for AI-generated outcomes.

To address these ethical concerns, organizations should establish multidisciplinary committees, including ethicists, healthcare professionals, and technology experts, to develop and oversee the ethical deployment of AI in healthcare. This can help ensure that AI applications align with the organization's values and adhere to established ethical principles.

By prioritizing data privacy, quality control, trust-building, domain expertise, regulatory compliance, and ethical considerations, organizations can unlock the full potential of generative AI and create a future in which technology and human expertise work hand in hand to improve healthcare outcomes and overall patient well-being.

Increasing interest in generative AI in the life sciences and healthcare industry has led to the use of numerous AI applications and the emergence of a great number of use cases. In our next blog, we will look at some promising generative AI applications in life sciences, like personalizing customer journey maps with ChatGPT, intelligent chatbot for real-time patient interaction, and more. Stay tuned!

Author

Pratik Maroo
Pratik Maroo

Insights to build #FutureReadyHealthcare