AI solves healthcare – COVID integration resolves pharma inefficiencies.

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 Goldman Sachs Research.

“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 Goldman Sachs’ 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.

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.