Healthcare, one of the largest sectors of the U.S. economy, is one of many industries with great opportunities for the use of artificial intelligence (AI) and machine learning (ML), says Goldman Sachs of the U.S. Biotech Sector. Lead analyst Salveen Richter says. research.
“We are in an exciting time of convergence between two important economic sectors, technology and healthcare. To Richter, one of the authors of her in-depth Byte-ology report, which includes contributions from Goldman Sachs’ medical and technology research team, her integration of AI/ML into healthcare, the technology’s most promising applications, and the status of venture capital funding in the field of “byte studies”.
Why is healthcare ripe for disruption?
Combining massive multimodal datasets in healthcare with AI/ML’s competitive advantages in efficiency, personalization and effectiveness, we believe we are poised to make a revolutionary wave across healthcare. I’m here.
From a data perspective, the healthcare industry generates and relies on large amounts of data from various sources. This creates a rich environment for applying AI and ML. Given the inefficiencies of the healthcare system, the need for these technologies is there. Estimated to take him more than eight years and more than $2 billion to develop the drug, he is only one of 10 candidates to gain regulatory approval and is likely to fail. is very high. AI, including generative AI, is one of the technologies with the potential to create safer, more effective medicines and streamline personalized care.
The bottom line is that we must assume that we are in an exciting time of convergence between two major economic sectors, technology and healthcare, and that a wave of innovation will emerge from it.
How is AI already changing the healthcare industry?
Some of the earliest applications of AI in healthcare were diagnostics and devices, including areas such as radiology, pathology, and patient monitoring. Dating back to 1995, the PAPNET Testing System, a computer-assisted cervical smear rescreening device, was the first FDA-cleared AI/ML-enabled medical device. In the 2000s, other approvals included digital image capture, cell analysis, bedside monitoring of vital signs, and predictive warning of accidents that might require medical intervention.
Big tech companies are also involved, entering as cloud solution providers and applying their technical expertise in areas such as wearable devices, predictive modeling and virtual care. One of the much-discussed achievements concerns his deep-learning algorithm, which effectively solved the decades-old problem of predicting the shape in which a protein will fold based on its amino acid sequence, which is essential for drug discovery. bottom.
Where is the integration of AI into the healthcare sector today?
Despite all the previous innovations, we are still in the early innings. The potential of AI/ML in healthcare has been around for decades, but we believe its role came to the limelight during his response to the Covid-19 pandemic. AI has helped companies develop his Covid-19 mRNA vaccine and treatment at unprecedented speed. Additionally, the Covid-19 pandemic represents a significant inflection point for telemedicine and remote monitoring, highlighting the need for digital solutions in healthcare to improve patient access and outcomes.
These successes demonstrate the clear benefits of incorporating AI/ML and other technologies to improve patient outcomes at a much faster rate than would be expected with traditional methods, and thus a strong move into the field. I think it made me even more enthusiastic.
What are some of the more promising AI-driven applications that could emerge in healthcare in the near future?
Our latest Byte-ology report outlines technologies that have the potential to transform healthcare, including 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: Drug development uses AI/ML to identify new targets, design drugs with favorable properties, predict drug interactions, and replace the costly traditional methodologies of wet lab trial-and-error development. You can minimize your need.
Are there areas of medicine more likely to benefit from AI than others?
AI/ML use cases can be found in almost every segment of healthcare. The difference is how much or how long it has been in use in a given sector, how validated the use cases are, and how difficult new technological advances are to implement. healthcare system. For example, while we have a history of using AI tools in radiology and pathology, many are using them in areas such as drug design, predicting which patients are most likely to respond to a particular drug, and digitizing labs. We believe we need more solid evidence to understand the AI/ML benefits of
Even in areas where AI/ML adoption is in its early stages, we believe that the potential benefits of AI/ML will not be ignored and will be studied in detail and implemented more and more over time. From regulatory support, standardized benchmarks to measure performance, public forums to improve collaboration and transparency, and importantly, proof-of-concept with proven benefits to patients and healthcare professionals, uptake will benefit greatly.
What are the barriers and hurdles of AI in healthcare?
There are cultural obstacles, such as the healthcare industry, which relies on patents and monopoly rights. This raises questions about how to protect IP without slowing progress, and how to share information like software engineering research that benefits from open source data.
Hesitation to AI/ML stems from the need for better surveillance systems to protect patients from hacking and compromise events, the lack of ongoing education of medical professionals on the benefits of these technologies, and the AI/ML model. may be exacerbated by concerns that It is susceptible to bias as a result of past underestimations embedded in the training data.
Finally, some stakeholders are taking a “wait and see” approach, waiting until there is hard evidence that benefits are being achieved before investing the resources necessary to incorporate these technologies. There is a possibility that
Are there specific uses or benefits of generative AI, especially in healthcare?
Generative AI, including ChatGPT, opens up myriad opportunities in healthcare, such as generating synthetic data to aid in drug development and diagnostics. Otherwise, data collection can be expensive or scarce. Some examples here include developing models to generate synthetic abnormal brain MRIs for training diagnostic ML models and zero-shots for generating new antibody designs that differ from those found in existing databases. Includes use of generative AI.
Generative AI can also help design new drugs, repurpose existing drugs for new indications, and personalize treatment plans by analyzing patient-centric factors such as genetics and lifestyle.
ChatGPT specifically performs administrative tasks such as scheduling appointments and drafting insurance approvals to free up physicians’ time, assist healthcare professionals by conveniently summarizing scientific literature, and provide conversational patient Can be used to improve patient engagement and education by answering questions.
It has also been suggested that ChatGPT could be theoretically useful for clinical decision-making such as diagnosis, but the risk of hallucinations must be considered for ChatGPT to build sufficient reliability and validation for this application. This may take some time. looks plausible.
What is the status of VC investments in healthcare AI? How does GS evaluate these companies?
Venture capital funding continues to support and foster innovation in both early- and later-stage private biotechnology companies. In 2022, VC funding for AI- and ML-powered healthcare companies continued to grow, even as it declined amid a market downturn and consequent slowdown in VC funding. So far in 2023, amid recession risks and other headwinds, VC deployments in healthcare AI have slowed, as elsewhere.
Given the potential benefits of AI/ML in efficiency and effectiveness, how companies leverage the available and rapidly expanding technological arsenal is a key competitive differentiator. It’s a part. Competitive differentiation, such as the quality of the management team, the ultimate goal of the platform, the timeframe for investors to understand if this goal has been achieved, how the platform integrates available AI/ML, etc. We consider many factors when evaluating A toolkit with unique technology to defend against emerging players.
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