Investors are Pouring Cash Into AI Startups Focused on Health Care
Grow Your Business, Not Your Inbox
A recent report from CB Insights found that healthcare Artificial Intelligence startups have raised $4.3 billion across 576 funding rounds in the last five years – more than any other sector. Investment flowing into building AI that works with people to tackle healthcare issues will continue globally. Meanwhile, finding sustainable answers to tragic conditions like Alzheimer’s Disease will require accurately kept health records to advance progress -- and take the willing participation of people whose lives are fatally impacted by the disease. The party ultimately responsible for finding the answer to Alzheimer’s might not be human -- or at least, the effort to rid the world of the disease may not be fully a human one.
Artificial Intelligence presents the medical field with new opportunities to use learnings from existing and newly created data sets to solve complex human issues over the next few years. The technology’s complementary utility for health science and medical research offers new opportunities to unearth minuscule clues from individual patient histories that lead to global breakthroughs. AI has the potential to serve as a natural partner for medical researchers and professionals who spend careers combing through records to uncover trends and anomalies.
AI helps people find medical answers.
As an industry, health science is beginning to realize the full benefits of using precision medicine to treat disease. Early success stories include making progress in cancer detection and uncovering potential health indicators from medical histories and DNA analysis. The underlying idea of using AI for health science, in particular, is to look at people’s specific genetic or molecular profiles and determine what personalized treatment works best on a case-by-case basis.
In the coming years, successfully advancing precision health science will depend on collecting and storing data representing diverse patient populations. It will also rely on the health science sector’s ability to develop sophisticated AI and machine learning algorithms that mine massive amounts of data to answer very specific healthcare questions. Questions like: how do we find the indicators hidden in countless health records? Which genetic variants matter? Why does one disease impact a patient and not someone with a similar genetic makeup? AI can serve as as a means to helping the health science sector answer some of these questions, analyze specific factors with precision and bring clarity to patients earlier in the diagnosis discovery process.
AI's real world impact across health sectors.
AI’s real world impact on health science has already materialized in the form of new pharmaceutical combinations, more promising hypotheses, improved medical diagnostics, targeted risk factor analysis and reporting that leads to more accuracy in personalized medicine. AI can fully absorb, contextualize and analyze critical healthcare information in the time it takes a human counterpart to read through a few records. The technology is built to mobilize and manage large data sets autonomously. Meanwhile, human counterparts can focus on communicating the benefits of AI findings, proactively using them to address individual medical concerns and offer more personalized patient care.
AI can integrate data from multiple sources and determine relevance to specific cases swifter than humans. The technology can analyze data in real-time and produce actionable insights that would take several hours -- or years in some cases -- for people to complete. When built responsibly using objective data sets and lab-tested technology, AI does not have preconceived notions about the medical records, DNA and RNA analysis and general information it sorts through, eliminating potential biases and erroneous conclusions.
AI’s health science success hinges on the availability of human-curated training data sets that allow for performance and bias testing prior to AI entering the market. The opportunity to connect AI and countless data sets presents the greatest opportunity for medical professionals looking toward technology for answers. In practice, AI’s core ability to automate data analysis frees up medical research people to focus on the end result, apply findings to real world medical or pharmaceutical trials, and, ultimately, adapt individual healthcare plans to incorporate new methods.
Looking ahead to an uncertain future.
The biggest challenge for health practitioners turning to AI in 2019 will remain the availability of curated data sets needed to train algorithm-driven technologies destined for disease detection and other crucial medical work. AI must be trustworthy enough to make accurate predictive assessments that dramatically impact patient care and health results in the real world. The process of preparing AI for health will become easier in the near future as the technology advances, regular people become more familiar with AI and its real world applications for disease prevention prove successful.
After all, disease prevention is the holy grail. Technologies, like AI, that enable early disease detection and interception will transform patient care wholesale. AI can help medical professionals detect diseases earlier and give people impacted by those diseases a fighting opportunity to overcome them.
Undoubtedly, human efforts to rid the world of Alzheimer’s disease, and other deadly illnesses or inherited conditions, will advance with the support of data-driven technologies. Tapping AI for those tasks will allow doctors and medical professionals to focus on providing more precise and empathetic patient care. Researchers can spend time making sense of AI-driven findings in order to bring machine-discovered remedies into a very human reality, like living with Alzheimer’s, that changes lives -- and saves them.