DECODE – A Closer Look at AI for Pharmaceuticals

Since the beginning of last year, the world has been consumed by its fight against COVID-19, giving machine learning (ML) and artificial intelligence (AI) technologies an unprecedented chance to make their marks on healthcare. From the quick discovery of trends in disease, to supporting hospitals operating at full capacity, and critical roles played in vaccine acceleration, we’ve seen a paradigm shift in how our industry uses ML and AI.[1] Given the fast trends we are experiencing, we must have conversations about these technologies and their potential to improve health outcomes for patients.

The Cambridge Innovation Institute recently launched DECODE: AI for Pharmaceuticals, the organization’s first cross-functional conference to uncover AI's true value for the pharmaceutical sector and bring the industry together to share learnings and collaborate with the aim to achieve scalable AI for maximum gains. Johnson & Johnson Innovation is proud to be a part of this important conversation. Emma Huang, Senior Director of Data Sciences External Innovation at Johnson & Johnson Innovation, recently joined the DECODE advisory board and will speak at the conference on the topic of ‘Effective External Collaboration & Building the Right Partner Landscape’.

We sat down with Emma Huang and Cambridge Innovation Institute Senior Event Director, Dominie Roberts, to learn more about DECODE and AI’s integration into healthcare.

Emma Huang

Emma Huang

Dominie Roberts

Dominie Roberts

Q: Dominie, can you please tell us about the Cambridge Innovation Institute and your latest initiative, DECODE: AI for Pharmaceuticals?

Dominie: Of course! Cambridge Innovation Institute (CII) works with the goal to deliver cutting-edge information in research while facilitating opportunities for professionals in the life sciences, healthcare, and technology. We aim to accomplish this through our events, training platforms, and writings that bring focus to areas we feel are critical to the advancement of innovation.

Our event, DECODE: AI for Pharmaceuticals, is happening June 7-9 and we’re really excited to be the first cross-functional conference to host talks about AI’s potential impacts for the pharmaceutical industry. By unifying voices in innovation, data science, digital health, and information technology, we intend to have deep conversations about where AI’s true value lies and how companies can implement it into their business strategies. DECODE has something for everyone, and we’re especially excited about some of our upcoming talks like ‘Building your AI-Ready Data Strategy’ and ‘Cross-Functional AI Use Cases and Implementation Best Practices’.

Q: Thanks, Dominie! And what was CII’s motivation behind the launch of this new conference? What makes it different from your other offerings?

Dominie: For many years, CII has been a host to many important conversations and research collaborations for AI, specifically those focused on bench-level applications in our life science portfolio. Committed to meeting the industry’s evolving needs, we launched DECODE to provide a platform that creates a forum for voices throughout the sector with the aim to have all-encompassing conversations about AI and its uses for pharmaceutical organizations.

Q: And Emma, you recently joined DECODE’s advisory board for AI in Pharmaceuticals. Can you share more about the board’s mission and your role in the position?

Emma: Absolutely! This was a great opportunity to help shape the agenda for this novel forum and to join conversations at both DECODE and its preceding event, AI Innovation Circle. The AI in Pharmaceuticals advisory board is made-up of a fantastic group of peers from across the industry who are eager for the changes AI could bring to pharmaceuticals, and cognizant of the challenges it can bring along the way. It has been great to be part of a group that is working hard to break down these siloes, both within our own organizations and among the many stakeholders in healthcare, and to have meaningful conversations that will hopefully lead to our shared understanding of AI.

Q: Emma, what specific trends or shifts have you been seeing in the space that interest you? How do you think AI will create better health outcomes for patients?

Emma: Having worked in early innovation for data sciences over the past few years, it has been impressive to see how quickly some areas have taken up AI for drug discovery and development. One example is the growing trend of using AI to analyze imaging data in all phases of disease: early diagnosis, clinical endpoints, disease progressions, and even treatment responses.[2] As we move beyond the scientific development of new algorithms, we now face new challenges in moving those algorithms to clinical deployment to deliver value for patients. While AI is creating a new set of challenges for our industry, it is also bringing us closer to our goal of creating the best possible health outcomes for our patients. In the long run, we hope that AI will enable us all to do more with the resources we have, whether it be accelerating new therapies, democratizing access to medical expertise, or empowering patients to manage their own healthcare more actively.

Q: Finally, Dominie, are there any areas in AI that CII hopes to explore in the future?

Dominie: AI saw recent advancements in its fight against COVID-19, but there is still a long way to go before we can use it at its greatest potential. Outdated innovation strategies and data models must be redefined, and we believe that platforms fostering collaboration in our industry will be critical to facilitating this evolution. Looking ahead, we see huge value in collaboration between industry and new innovators, and we are working hard to continue growing our portfolio of partnering events to make this a reality.

We are grateful to Emma and Dominie for speaking with us. They’ve given us some great insights and we look forward to hearing the powerful conversations coming up at DECODE and AI Innovation Circle.

If you are in the field of healthcare or life sciences and are interested to learn more about the integration of artificial intelligence into our industry from Emma or other thought leaders, consider registering for these upcoming events at: www.decodeaipharma.com.

Disclaimer:

The information in this interview is for informational purposes only. The information may be either proprietary or licensed to Johnson & Johnson Innovation LLC, its parent company or any affiliate or subsidiary companies ("JJI and Johnson & Johnson ") or is provided by or proprietary to third parties. In case of third-party information, please note that only those third parties that provided or own this information are responsible for it. By providing this information, JJI and Johnson & Johnson are not affiliated with nor endorses these third parties or any of the information provided in this interview. This content is not intended to influence the use, sale, recommendation, or promotion of any products or services of JJI and Johnson & Johnson. JJI and Johnson & Johnson are not responsible or liable for any actions you do or do not take in relation to this information and are not responsible for any use of this information by you. By providing the information in this interview, JJI and Johnson & Johnson do not make any representations, warranties, or assurances, express or implied, as to the content or the information presented in this interview and are not responsible for any use by or alterations of this information by you. JJI and Johnson & Johnson hereby expressly disclaim any and all guarantees, representations, warranties, or assurances and assume no responsibility or liability, direct or indirect in relation to the information in interview.


[1] Arora, Neelima, et al. “The Role of Artificial Intelligence in Tackling COVID-19.” Future Virology, vol. 15, no. 11, 26 Nov. 2020, pp. 717–724., doi:10.2217/fvl-2020-0130.

[2] McCall, Becky. “COVID-19 and Artificial Intelligence: Protecting Health-Care Workers and Curbing the Spread.” The Lancet Digital Health, vol. 2, no. 4, Apr. 2020, doi:10.1016/s2589-7500(20)30054-6.