Major rethink

Generative AI (GenAI) has recently exploded onto the technological scene, disrupting industries and revolutionising the way we approach content creation.

As technologies like ChatGPT, Bard, EinsteinGPT, and others increase in prominence, the global market for GenAI is projected to reach an astonishing US$110.8 billion by 2030.

The surge in interest raises pertinent questions about how the technology works, its potential benefits and risks, and the ethical considerations that must guide its implementation.

GenAI utilises diverse technological approaches, such as deep learning, reinforcement learning or transformers.

The most common method involves deep neural networks, which analyse vast amounts of data to identify patterns or structures within the dataset.

Notably, the quantity of data required for training these large language models can be colossal, with generative AI systems requiring petabytes of data across various domains for effective training. Meta’s Llama 2 model reportedly required 70 billion parameters.

Smaller language models, however, can be trained on smaller data sets. What remains key, regardless of the data volume, is the data quality.

Generative AI has clear promise to answer complex questions, provide translation services and even serve as a 24/7 communication tool. But, the exponential growth of the GenAI market has surfaced some fears and concerns as the public contends with the promise and the potential peril.

Considering these challenges, life sciences organisations must establish a framework for the responsible usage of GenAI.

This framework can help establish organisational oversight, ensure compliance with regulations, develop consistent operations and infrastructure to enable GenAI in the organisation as well as foster a culture of responsible GenAI use.

Organisations embracing responsible use of GenAI can proactively mitigate the risks of GenAI, and address the public’s concerns while also leveraging the promise of GenAI.
Importantly, a comprehensive framework for responsible use of GenAI will also ensure it aligns its AI strategy to the overall business values and strategy.

At SAS, we have developed an approach to responsible innovation by focusing on our core principles of human-centricity, transparency, robustness, privacy and security, inclusivity, and accountability.

These principles guide both our internal uses of AI as well as our development of AI and analytics solutions for the market.

While each life science organisation may have its own values and principles, these six principles can act as a guide for organisations to develop their own framework for responsible innovation.

The principle driven approach guides organisations in adopting GenAI technologies while minimising potential risks and ensuring positive outcomes for individuals and society and aligning their AI strategy with organisational values.

Though there are risks, GenAI’s impact in the life sciences industry holds significant promise, with examples showcasing its potential in various areas.

Beyond general applications, such as accelerating clinical development and transforming application development and testing, GenAI stands to revolutionise life sciences processes in profound ways.

For instance, in clinical development, generative AI could expedite tasks across the entire life cycle bringing more services within the four walls of biopharma companies, thereby improving experiences for both talent and patients and contributing to more efficacious therapies.

Additionally, in marketing excellence within life sciences, GenAI could enable hyper-personalised content at scale and as a result improved measurement of sales effectiveness.

As organisations in the life sciences sector embrace generative AI, it becomes imperative to not only recognise the potential benefits but also adhere to a responsible adoption framework grounded in ethical principles.

By doing so, pharma can effectively navigate the dynamic landscape of GenAI, harness its transformative potential and simultaneously mitigate potential risks, ensuring positive outcomes for both organisations and society at large.

Marinela Profi is Generative AI Product Strategist at SAS. Go to sas.com