Artificial Intelligence in Life Science — Demystifying the Buzz

Artificial Intelligence in Life Science — Demystifying the Buzz

This article was originally published to Bruno’s LinkedIn.

The Biotechnology Innovation Organization held its annual convention digitally this year, as part of which I had the opportunity to join the panel, “Demystifying Buzzwords – How Artificial Intelligence and Machine Learning are being used Now in the Life Sciences” with esteemed professionals [1] from all corners of the biotechnology industry.

The discussion is well-timed. Over the past decade we have seen the convergence of massive data sets, near-unlimited computing power, and advanced data science. The most pioneering biotechnology companies have moved beyond the hype. They now use AI and ML to turbo-charge efficiencypower up knowledge, and cut time from data to evidence.

Turbo-Charge Efficiency

AI does more than making a biotechnician’s job easier. The ability to analyze a torrent of data and relate it to tricky questions and challenges allows us to reach new depths and heights. AI and ML broaden our horizons as well. We might all need to become data scientists at some point to leverage the wealth of opportunities.

AI and ML serves as a “force multiplier” as Krishnan Nandabalan described it. After hitting a bottleneck back in 2015, InveniAI started using AI and ML to automate steps which led them to improved efficiency and enhanced analytical capabilities. Similarly, at Centrexion, AI and ML helped Kerrie Brady and the team to “bite big and chew hard” so even as a team of 6, they were able to manage five development projects.

When AI and ML apply to every part of the value chain, these technologies can promote efficiency, effectiveness, and increase probability of success. At Novartis, we are embedding AI and ML across the drug development value chain. We have many initiatives to maximize the value of these technologies to make sure they are not just widespread but also have depth.

Power Up Knowledge

Some professionals think that AI doesn’t just stand for Artificial Intelligence — it is an Accelerator of Innovation. In describing this technology, Moira Gunn said, “There’s no doubt about it; once you’re able to grasp it and put it to work, it accelerates innovation as we know it. That’s brand new, and that’s one of the reasons that our whole industry could change. Not from a test tube, but from data and from information.”

Some also define AI as Augmented Intelligence. AI doesn’t exist and operate in a vacuum; humans are critical to assess the reliability of the input and output, and synthesize collected data further. Aashish Kachru addressed this symbiotic relationship and advised us to “Embrace the job displacement that’s going to come as a result of AI, and move yourself towards higher skill values where you’ll be needed.”

The current global pandemic creates one such circumstance in which higher skill values are necessary. COVID-19 has led to a great deal of renewed connectivity and openness across the pharma industry. Technology such as Natural Language Processing has enabled this effort, scanning millions of publications and tapping into global knowledge to answer an individual team’s questions. An individualistic line of thinking is far too narrow when it comes to AI and ML. The panel highlighted the opportunity to refine the life sciences ecosystem, allowing companies to leverage their strengths and pool decentralized knowledge, as it can help individuals and healthcare professionals make the best informed decisions.

Cut Time from Data to Evidence

Novartis’ data42 program applies advanced analytics to derive medical insights from 2 million patient-years of data. We have primarily focused on bringing that data together and making it AI and ML-ready. These insights contribute to our increasing understanding of diseases and medicine, thereby enhancing R&D decision-making and ultimately #reimagining drug discovery and development by cutting the time from data to evidence.

But it all started with data. This is the condition for knowledge-workers really to be knowledge-workers, as opposed to data janitors and information engineers, creating room for operational, analytical, and experiential value growth, thereby expanding our capabilities.

According to multiple analyses, it can take over a decade to bring a new drug to patients, and only one out of ten drugs is successful. Early AI and ML opportunities have shown the potential to cut years off this timeline and maximize the probability of success. For which, as several panelists pointed out, making AI and ML part of the value chain end-to-end is the key.

Conclusion

The hype that AI and ML are disrupting the way we work is true, to an extent. AI and ML lead to reduced company costs and improved customer experiences, while Price Waterhouse Coopers reports that AI alone is expected to have a $15.7 trillion economic impact by 2030.

Completely in line with Brian Martin stating that “we have moved from myth to value”. As he elaborated, AI and ML “deliver the momentum to change”. The life sciences has become a digital industry powered by AI and ML. For us at Novartis, it is not “if” or “when” AI and ML will help us in our commitment to reimagining medicine. Now, the focus is on scaling it across the entire organization to fuel our unbossed, inspired and curious culture.

I’d like to extend a word of thanks to my esteemed co-panelists, all of whom shared great insights that did wonders to demystify AI an ML and I look forward to continuing and broadening these conversations.

[1] NPR’s Tech Nation host Moira Gunn moderated the panel, which included Altruista Health CEO Aashish Kachru InveniAI LLC President and CEO Krishnan Nandabalan, GSK Consumer Healthcare Director of Search and Evaluation Michael Keane, AbbVie Head of AI in R&D Information Research and Senior Principal Data Scientist Brian Martin, Centrexion Cofounder and former CBO Kerrie Brady, and myself.

Connectivity and Openness in the COVID-19 Era

Connectivity and Openness in the COVID-19 Era

This article was originally published on Bruno’s LinkedIn

Many journalists and bloggers use “unprecedented” to describe our current situation. No word is more appropriate. Communities around the world are engaging in an epic struggle to mitigate the spread and impacts of COVID-19. We are indebted beyond reconciliation to healthcare professionals and caregivers working on the front-lines of the pandemic. There is much to learn from the COVID-19 outbreak. In particular, two lessons cannot escape attention: 1) Embracing data science and digital technology is no longer optional for our healthcare systems, and 2) Open collaboration and innovation are essential to #reimaginingmedicine.

Collaboration Between Life Science Companies

It is clear that COVID-19 is not a sequestered issue—it is a global pandemic that has traveled far and wide to impact some of our world’s most vulnerable populations. For this reason, the collaborative efforts of life science companies as they work to minimize and mitigate the overall impact of the novel coronavirus was much needed and unheard-of. The Bill and Melinda Gates Foundation’s efforts to pool resources across 15 life science companies is especially notable. This group will share their libraries of resources, which include unique molecular compounds that could potentially turn the tides in the public health sector’s favor. Those molecular libraries have found a home in the COVID-19 Therapeutics Accelerator, a platform developed by the Gates Foundation. Researchers use this tech to quickly identify which compounds and options are most likely to make a positive impact. Combined with accelerated vaccine and therapeutic trials and the combination of data from a multitude of countries and sources, the initiative has the potential to create a step-change on both national and international levels.

Collaboration Between Healthcare Providers and Tech Companies

Stay-at-home and quarantine orders are limiting people’s access to healthcare around the world. For some people,healthcare providers are able to visit them at their home, but this is not an option for most. Where home delivery is not possible, even access to medication is disrupted. Technologies such as home assistants and telemedicine are enabling healthcare providers to reach their most vulnerable patients without making direct, person-to-person contact. Hospitals are using AI combined with sensors for a variety of tasks, from tracking patient temperatures to detecting acute respiratory conditions. These technologies allow patient monitoring without putting healthcare providers at more risk for infection. Providers have utilized drones to reach those who cannot leave their homes whether due to compromised immune systems or mild symptoms and to drop off medical supplies from rural locales in Ghana to cities across the United States. Their service include prescription drugs, medical implements such as swabs and masks, and even COVID-19 testing kits. Organizations, including the World Health Organization, IBM, Oracle, Microsoft, and other tech companies, government agencies, and international health organizations are partnering in building the blockchain-based open data hub called MiPasa. It is poised to enable rapid and precise detection of COVID-19 carriers and infection hotspots around the world.

Health Authorities’ Overall Collaborative Position

As the crisis has played out, health authorities have seen the fruit of their digitalization efforts and are doubling down on these technologies – publishing and advocating positions that favor even further acceleration. The CDC, FDA and WHO, have recognized that digital health technologies can provide powerful tools for public health officials and the public in the management of the COVID-19 response. GermanySwitzerland and many more countries launched a dedicated hackathon in response to COVID-19. The Health Innovation Hub, established by Germany’s Ministry of Health, published a list of trusted telemedicine services. The Mayo Clinic and the Minnesota state health department developed an artificial intelligence-powered tool to determine which areas of the state were most at risk for spreading and contracting COVID-19. The National Institutes of Health’s Accelerating COVID-19 Therapeutic Interventions and Vaccines initiative launched in an unprecedented effort to bring together health agencies and members of the pharmaceutical industry in response to the current and future pandemics.

Connectivity and Openness, the “New Normal”?

As we begin to re-open businesses and schools, we should remember the value of collaboration within the healthcare ecosystem. Resource pooling, information accessibility, and new technologies played an important role in combating COVID-19. As usual, hindsight is “20/20” – it is hard to fathom what prevented us from unleashing this level of collaboration earlier. When we needed to stay physically apart, we saw the urgency to come together to combine our knowledge, skillsets and experience as illustrated above, but also in so many other examples.

These unprecedented times unearthed the need to move beyond the usual calls for open innovation: a need for connectedness, maybe deriving from Joy’s law, “no matter who you are, most of the smartest people work for someone else”. The state of urgency side-showed the prime focus on internal excellence, intellectual property protection and competitive vying and brought together people in totally new ways. I can not help asking, “what if” this connected openness, this #unbossed can-do mindset, were to become the real “new normal”?