In the last few months, I’ve enjoyed speaking at several events on new forms of human + machine collaboration, the impact this is having across industries, and the potential impact in healthcare.
In November, I spoke at the Clinical Innovation Summit, an event hosted by Accenture and Merck. This event was comprised of nearly 100 senior executives from companies that are members of our Life Sciences Cloud Coalition, which provides attendees the opportunity to share key experiences and best practices, to foster relationships, and to see demonstrations of new technology emerging in our industry.
Earlier this year, I presented a keynote at Medidata NEXT in New York—an event attended by more than 2,000 life sciences professionals who come together to connect with, learn from and be inspired by an array of industry thought leaders and key influencers from top pharma, biotech and medical device companies.
At these events, two key themes were evident:
A fundamental acceptance of the value digital can provide in clinical development, particularly in patient-facing applications
A heightened sense of urgency to leverage digital to increase the pace of research, as well as improve participation, diversity and engagement in clinical trials
Artificial Intelligence (AI) was core to most of these discussions, the main question being, “How do we adopt AI?” One of the primary concerns is the perception that AI is a “black box” which lacks transparency and as a result, may be difficult to validate. Indeed, new regulatory requirements in Europe attempt to address this in a broader sense by specifying that where an AI uses personal data to make decisions, companies must be able to explain the logic behind the decision-making process. This is only part of the answer but, along with broader moves across industries towards transparency, should help data scientists looking to adopt AI in clinical research and development.
Three Trends to Watch
When it comes to new forms of human + machine collaboration, three key trends are driving innovation and creating opportunities to generate new data and insights for researchers across industries, including healthcare:
The growth of smart devices, objects and products. The trial of the future will leverage more than the smartphone and connected health device; think about smart-clothing that tracks our activities and biometrics, smart mirrors that can detect changes in skin condition and eye health, and digital pills that can track our consumption of, and reaction to, a therapeutic.
New forms of interaction with those devices. The trial of the future will not require a keyboard (even on a smartphone); we will see increasing use of chatbots, conversational voice-first technology, avatars, touch and gesture interfaces. These advances will help improve participation and diversity, particularly among patient populations who may have difficulty using a smartphone.
New forms of AI will underpin everything. New forms of collaborative AI will be developed focused on bridging the gaps between humans and technology.
The combination of these three advances will improve healthcare delivery, while providing new data and insights that will inform clinical research and development. According to Accenture analysis, the potential annual value of the top ten AI applications in healthcare could reach $150 billion by 2026.
Looking to learn more?
To learn more about AI’s potential impact on life sciences:
Take a look at the Life Sciences Technology Vision 2018.
Check out the book Human + Machine: Reimagining work in the age of AI by Paul R. Daugherty and H. James Wilson, which discusses many of these ideas.
Or reach out to me directly if you’d like to discuss any of these ideas in further detail.