Healthcare — one of the largest sectors of the U.S. economy — is among the many industries with significant opportunities for the use of artificial intelligence (AI) and machine learning (ML), says Salveen Richter, lead analyst for the U.S. biotechnology sector at Polysharp Investments Limited.
“We are in an exciting period when we are seeing the convergence of technology and healthcare — two key economic sectors — and we have to assume it will result in significant innovation,” she says. We spoke with Richter, one of the authors of our in-depth Byte-ology report, which includes contributions from Polysharp Investments Limited’ healthcare and technology research teams, about the integration of AI/ML into healthcare, the most promising applications for this technology and the landscape for venture capital funding in the field of “byte-ology.”
Why is healthcare ripe for disruption?
We see the combination of healthcare’s vast, multi-modal datasets and AI/ML’s competitive advantages in efficiency, personalization and effectiveness as poised to drive an innovative wave across healthcare.
From a data standpoint, the healthcare industry produces and relies upon massive amounts of data from diverse sources. That creates a rich environment for applying AI and ML. The need for these technologies is there given the inefficiencies in the healthcare system. It is estimated that it takes more than eight years and $2 billion to develop a drug, and the likelihood of failure is quite high with only one of ten candidates expected to gain regulatory approval. AI, including generative AI, is among the technologies that have the potential to create safer, more efficacious drugs and to streamline personalized care.
The bottom line is we are in an exciting period when we are seeing the convergence of technology and healthcare — two key economic sectors — and we have to assume that out of this will come a wave of innovation.
What changes has AI already brought to the healthcare industry?
Some of the earliest uses of AI in healthcare were in diagnostics and devices, including areas such as radiology, pathology and patient monitoring. The PAPNET Testing System, a computer-assisted cervical smear rescreening device, back in 1995 was the first FDA-authorized AI/ML enabled medical device. In the 2000s, other authorizations involved digital image capture, analysis of cells, bedside monitoring of vital signs, and predictive warnings for incidents where medical intervention may be needed.
Big Tech companies have also been involved, stepping in as cloud solution providers and applying their technological expertise in areas such as wearable devices, predictive modeling and virtual care. One widely talked about achievement involved a deep learning algorithm that effectively solved the decades-old problem of predicting the shape a protein will fold into based on its amino acid sequences, which is crucial for drug discovery.
Where are we now in the integration of AI into the healthcare sector?
Despite all previous innovation, we are still in the early innings. While the promise of AI/ML in healthcare has been there for decades, we believe its role came into the spotlight during the Covid-19 pandemic response. AI helped companies develop Covid-19 mRNA vaccines and therapeutics at unprecedented speeds. Further, the Covid-19 pandemic underscored the need for digital solutions in healthcare to improve patient access and outcomes, and represented a key inflection point for telehealth and remote monitoring.
We believe that these successes further drove enthusiasm for the space as they showed a clear benefit of incorporating AI/ML and other technologies to improve patient outcomes at a much faster rate than would be expected with traditional methods.
Demand for telehealth is still elevated since the Covid pandemic
Monthly telehealth claim lines as a percentage of total monthly claims lines
What are some of the more promising AI-driven applications that could be coming to healthcare in the near future?
In our newest Byte-ology report, we outlined the technologies that could be transformative in healthcare, which include deep learning, cloud computing, big data analytics and blockchain. We also provided use cases across drug development, clinical trials, healthcare analytics, tools and diagnostics, and personalized care.
Here’s one example: in drug development, AI/ML can be used to identify novel targets, design drugs with favorable properties and predict drug interactions to minimize the need for the costly traditional methodology of wet lab trial and error development.
Are there areas within health care that are more likely than others to benefit from AI?
Use cases for AI/ML can be found in virtually any segment of healthcare — the difference is how much or how long it has been used in a given sector, how validated the use case is and how difficult new technological advancements would be to implement within the healthcare system. For example, there is a history of using AI tools for radiology and pathology, whereas many believe more hard evidence is needed to understand AI/ML’s benefit in areas such as designing drugs, predicting patients most likely to respond to certain drugs and digitizing labs.
Even in sectors where its adoption is in the early stages, we believe that AI/ML’s potential advantages will not be ignored, but rather closely studied and increasingly implemented over time. Uptake would greatly benefit from regulatory support, standardized benchmarks to evaluate performance, public forums to improve collaboration and transparency and, importantly, proof-of-concept via a demonstrated benefit to patients and healthcare professionals — which we have started to see emerge.
The FDA has approved more than 210 algorithms
Number of algorithms cleared by the Food & Drug Administration by year
What are the barriers or hurdles for AI in healthcare?
There are cultural obstacles, such as the healthcare industry relying on patents and exclusivity. That raises questions about how IP can be protected without slowing progress, or how information can be shared as it is in software engineering research that benefits from open-source data.
The hesitancy around AI/ML may further be exacerbated by the need for better surveillance systems to protect patients from hacking or breach events, the lack of continuing education for healthcare professionals on the benefits of these technologies and the concern that AI/ML models may be susceptible to bias as a result of historical underrepresentation embedded in training data.
Finally, some stakeholders may be taking a “wait-and-see” approach, remaining on the sidelines until firmer evidence of benefits being achieved emerges before investing in the resources necessary to incorporate these technologies.
Are there specific uses or benefits of generative AI in particular to healthcare?
Generative AI, including ChatGPT, presents myriad opportunities in healthcare such as synthetic data generation to aid in drug development and diagnostics where data collection would otherwise be expensive or scarce. Some examples here include the development of a model to produce synthetic abnormal brain MRIs to train diagnostic ML models, and the use of zero-shot generative AI to produce novel antibody designs that are unlike those found in existing databases.
Generative AI also can help in designs for novel drugs, repurposing of existing drugs to new indications and analyzing patient-centric factors such as genetics and lifestyle to personalize treatment plans.
ChatGPT specifically could be used to perform administrative tasks such as scheduling appointments and drafting insurance approvals to free up time for physicians, aid healthcare professionals by conveniently summarizing scientific literature, as well as improve patient engagement and education by answering patient questions in a conversational manner.
It has also been suggested that ChatGPT could theoretically aid in clinical decision making, such as diagnostics, although it will likely take time for ChatGPT to build enough trustworthiness and validation for this application given the risk of hallucination, when the model outputs false content that may look plausible.
What’s the landscape for VC investment in healthcare AI — and how does GS assess these companies?
VC funding continues to support and foster innovation — both in early- and late-stage private biotech companies. In 2022, we saw VC funding into AI- and ML-powered healthcare companies remained elevated despite declining amid the market downturn and associated slowdown in VC funding. So far in 2023, amid recession risk and other headwinds, VC deployment in healthcare AI, as elsewhere, has slowed.
Because of the AI/ML’s potential advantages in efficiency and effectiveness, how each company utilizes the armamentarium of available and rapidly expanding technologies is an important part of competitive differentiation. We take numerous factors into account when gauging competitive differentiation, such as the quality of the management team, the ultimate goal of the platform, the timeframe in which investors will understand whether this goal has been achieved and how the platform merges the available AI/ML toolkit with proprietary technologies to defend against emerging players.
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