Leveraging and Taming AI in Healthcare and Life Sciences

Written by: Henrik Jensen, Senior Director, Strategic Engagement

A Journey in Transformation and Governance

AI is changing more than just how people create content, code software, enhance customer engagement, and search the web. It’s changing entire industries, such as healthcare and life sciences.

In this article, we’ll explore how AI is doing nothing less than reshaping the future of medicine, from leveraging AI for clinical research collaborations to breakthroughs in life science. We’ll also dig into some of the challenges and ethical considerations surrounding this transformative technology.

Collaborating “with” AI for Clinical Research

The life sciences industry is undergoing a profound transformation as AI becomes more deeply integrated into clinical research, serving as a collaborator for many R&D professionals.

Spearheading such collaborations is the Drug Information Association (DIA), a trailblazer in harnessing AI to revolutionize healthcare and improve patient outcomes. Among its pioneering achievements, DIA has been instrumental in driving gene therapy advancements for diseases like tuberculosis and sickle cell anemia, offering potential cures where none existed before. By embracing AI, organizations such as DIA and the overall life sciences community are now capable of making significant strides toward conquering ailments that have plagued humanity for generations.

Through its application in clincal research, AI is poised to accelerate innovation and enable researchers to uncover breakthroughs in areas previously unexplored. However, ensuring diversity within clinical trials remains a challenge, and addressing this issue is crucial to harnessing the full potential of AI in healthcare. (Experts such as Junaid Bajwa, Chief Medical Scientist at Microsoft Research, have emphasized the pivotal role of diversity in clinical trials.)

Ensuring Ethical AI

While AI offers unprecedented opportunities, several challenges must be addressed to fully integrate it into the life sciences industry. High-quality data, diverse representation in clinical trials, and regulatory barriers are among the hurdles that must be overcome if AI is to be used to its fullest.

A notable challenge is the limited availability of shared big data sets for training AI models in life science, primarily due to the nature of the industry and its need for security around proprietary research and information. Additionally, the complex nature of human biology itself poses challenges as not all aspects can be accurately captured and modeled using AI algorithms alone.

Transparency in AI models being used in the industry is paramount for building trust in AI’s “results” and ensuring ethical practices in its applications.  Put another way, understanding how an AI model reaches its conclusions and total insight into its algorithmic decision-making process will be the critical factors in establishing AI as a credible and reliable tool for use in healthcare and life science.

The good news is that through collaborations like those spearheaded by DIA, AI is being put to good use already, enabling breakthroughs in clinical research and opening avenues for cures where none seemed possible. DIA’s commitment to innovation and patient-centricity ensures that we are on a path towards revolutionizing healthcare for the better.

In the larger healthcare landscape, while challenges persist, the future of AI in life sciences holds immense potential for improved patient outcomes, equitable healthcare systems, and a brighter future. By addressing these challenges and creating and adhering to ethical guidelines, we can harness the power of AI to shape a healthcare landscape that benefits all.