The AI Landscape in Health

AI is making its way into every facet of the healthcare and life sciences, from research and drug discovery to diagnostics and disease management. The use of AI in these areas dramatically increased in 2019. In Optum’s survey, 62% of who report having implemented an AI strategy—in 2018, only 33% reported implementation. According to the same study of healthcare executives, fifty percent of companies that choose to invest in AI see a return on investment in cost savings. Hospitals and health plans have a payback period of three years on AI investment, while biotech executives anticipate it for five years or more.

In the healthcare and life sciences landscape, AI is no longer a differentiator. It is an investment that many companies are taking to reap benefits (revenue, customers, savings) in a relatively short period. Also, given that AI tech is so widely available, if you can find the very in-demand data scientists required to build products, the use case and business model matters more than the AI algorithm.

AI in Health Research

There is a strong need to accelerate research to understand the mechanisms of disease better, establish biomarkers, and develop disease models. To fulfill such research needs, scientists are challenged with keeping up with the tsunami of biomedical literature that flows in daily. Anecdotes abound with wasted time and research dollars repeating work already done. Scientists are generating unprecedented volumes of data, and simplified access to the biological context found in scientific literature is needed. We need to empower scientists with AI tools that quickly assimilate, integrate, and present new (and old) knowledge.

Efforts go from general to very focused use cases. Quertle is an improved search engine meant to simplify the discovery process by integrating, organizing, and presenting biomedical literature. Genomenon’s Mastermind improves variant interpretation by searching for millions of scientific articles. Ingenuity Pathway Analysis attempts to predict downstream effects and identify new targets and biomarkers. Each offers a partial solution to a huge problem.

Natural language processing is at peak usefulness. It feels like every day a new language model is out in the world. OpenAI released its coveted GPT-2 model in the last few weeks. This model was trained on 40 terabytes of text and can answer questions, translate, and write stories with a bit of prompting. Without especially being instructed to do so (though the accuracy is not high), GPT-2 will summarize given text when prompted with “TL;DR.”

Summarization will be a critical use case in the biomedical field. Several efforts have focused on the use of NLP to mine PubMed,, and patents. The focus has been to find the right data and put it in front of the user. The best insights to drive health research will come from the unification of many sources and types of data, including published literature, genomics, gene expression, microbiome, electronic health records, social determinants, social media, wearables, and more. Natural language processing will be an essential component of this strategy to dynamically model evolving entities and relationships among many different forms of data.

AI in Drug Discovery

AI is supporting critical use cases to find novel drug candidates, cut costs, de-risk the drug discovery pipeline, and accelerate time to market. Drug discovery and development is a top focus of AI in life sciences. It is also a complex area of AI, and there are 100s of start-ups focusing in this area, along with significant commitments by established pharma leaders. The efforts span everything from using AI to design novel drug candidates, identify opportunities for drug repurposing, and in silico screening for efficacy and toxicity much faster than traditional R&D. Using AI to stratify patients to recruit and optimize clinical trials and analyze real-world evidence will also change the speed and success rate of drug development.

Google company Calico Labs is using AI, amongst other techniques, to tackle aging. One of their more notable projects is studying the age-defying naked mole rat that everyone should know about if only because evolution is so weird (here and here). Former Calico Labs chief computing officer, Daphne Koller, launched her own, Insitro, to lower the cost of drug development. In the same spirit, Atomwise provides ab initio drug screenings that boast of delivering accuracy comparable to wet-lab experiments.

Although decades away, quantum computing will transform the search for new drug candidates. It will enable us to design new drugs that aren’t possible today and will accelerate and de-risk the drug pipeline by rapidly solving for computationally intensive molecular interactions for toxicity and efficacy. In the case of precision medicine, it will make some of these complex algorithms to compute in real-time, possibly while the patient is still in the office.

Cloud Platforms for AI. Sometimes it feels like everyone has an algorithm, proof of concept, and a partnership to repurpose a drug or find that key disease target. The drug process could require lengthy connections. The real power will be easy-to-use tools that will allow everyone to do target and drug discovery on their own. Several companies are shifting to that paradigm. UK start-up, Lifebit, provides an enterprise platform to scale analysis within their own secure data environments. San Francisco-based BlackthornRX has neuroscience focused cloud-based computational platform for multimodal data collection, integration, and large scale analysis.

AI in Diagnostics and Disease Management

AI has the potential to accelerate and improve diagnostic accuracy for many disease processes. Deep learning can create applications to identify the patterns of disease in images and scans. Applications have been developed based on diagnosing issues in retinal images, tissue slides, CT images, MRI scans, x-rays, and facial features. The primary challenge to build these applications is the assembly of large data sets to train and validate models with images or scans representing both disease and non-disease. Most of these applications target the medical community, for use in hospitals and clinics.

Several new applications are targeted directly to the patient. These deep learning models train on speech, video, voice, social media activity, wearables sensor data, eye tracking, even keyboard usage patterns. Applications are already in use to detect abnormal heart rhythms, alerting of concerns that require medical intervention. The hope for the future is to turn our smartphones into personal medical assistants, with passive, unobtrusive monitoring and alerts of concerning patterns. To enable this next-generation functionality, we will need models that can learn from individual user patterns, synchronize signals from multiple sources, and identify abnormalities in real-time. To do so, the ability to train deep learning models on a smartphone will become common.

The challenges we see in introducing AI applications in high consequence environments, such as clinical decision support, are in user adoption. For a physician to trust and act on recommendations coming from an AI application, they need to know that the application has been validated prospectively via clinical trial. They must explain why a prediction matches a specific patient. The FDA has defined the clinical trial requirements for AI-based clinical decision support applications, considered Software as a Medical Device. To address the user adoption challenge, we need to make predictions more interpretable and understandable. Explainable AI is a necessary focus to drive adoption among physicians.

Cartoon from Loren Fishman

Bottom Line

The AI in healthcare will grow from $2 billion (2018) to $36 billion by 2025. AI has started to bring significant value to the life sciences and health care industries. The face of healthcare will completely change in the next five years because of AI and automation. Continued investments in natural language processing, quantum computing, deep learning, and explainable AI are needed to realize the potential of AI to maximize human health and reduce human suffering.

Written by

Angela Wilkins

I like science, machine learning, start-ups, venture capital, and technology.