When the world's largest healthcare company by revenue went looking for a technology solution that could improve quality of care while reducing costs, the search took ten years. What they found--an innovative way to model healthcare data--is saving the company an estimated $150M annually and enabling its medical professionals to provide accurate and effective care path recommendations in real time. This same solution, graph databases and graph analytics, proved crucial at the height of the Covid-19 pandemic. A testament to its potential, the market for graph technology is projected to reach $11.25B by 2030.[1] It's what social networking applications use to store and process vast amounts of "connected" data.

Turning the page on 2022 will be a cause for celebration in the healthcare sector. The past year was one of the worst financial years on record for hospitals, according to Kaufman Hall. New data from the healthcare consulting firm and the American Hospital Association indicates that 53% to 68% of the nation's hospitals will end 2022 in the red. At the same time, hospital employment is down approximately 100,000 from pre-pandemic levels. This is all happening amid a backdrop of growing margin pressures and an aging population.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Can artificial intelligence (AI) be legally listed as an inventor? After all, if AI can legally invent products, the number of patents on drug-discovery tools will shoot up fast. The issue is currently before a United States court. The U.S. Court of Appeals heard arguments on that question again last week, and the ruling could affect the pace of AI technology development, particularly within the pharmaceutical and life science industries.

A backdoor hidden in lab software that is activated when fed a specially crafted digital DNA sample. Typically, this backdoor would be introduced in a supply-chain attack, as we saw with the compromised SolarWinds monitoring tools. When the lab analysis software processes a digital sample of genetic material with the trigger encoded, the backdoor in the application activates: the trigger could include an IP address and network port to covertly connect to, or other instructions to carry out, allowing spies to snoop on and interfere with the DNA processing pipeline. It could be used to infiltrate national health institutions, research organizations, and healthcare companies, because few have recognized the potential of biological matter as the carrier or trigger of malware. Just as you can use DNA in living bacteria to hold information, this storage can be weaponized against applications processing that data.

CTO & Co-Founder of StreamSets, helping companies enable DataOps and deliver continuous data under constant change. The decline of their health, financial difficulty, personal safety risks and the loss of independence -- when it comes to aging, our seniors have a lot on their minds. In a strange juxtaposition, the advancement of our medical knowledge and technology can actually exacerbate the situation for them. Consider this -- the first electroencephalogram (EEG) was recorded in 1924. The first MRI was in 1977.

As organizations look to modernize and optimize processes, machine learning (ML) is an increasingly powerful tool to drive automation. Unlike basic, rule-based automation--which is typically used for standardized, predictable processes--ML can handle more complex processes and learn over time, leading to greater improvements in accuracy and efficiency. But a lot of companies are stuck in the pilot stage; they may have developed a few discrete use cases, but they struggle to apply ML more broadly or take advantage of its most advanced forms. A recent McKinsey Global Survey, for example, found that only about 15 percent of respondents have successfully scaled automation across multiple parts of the business. And only 36 percent of respondents said that ML algorithms had been deployed beyond the pilot stage.

Artificial intelligence and machine learning, are dominating every aspect of our lives. AI is used in various areas like healthcare, education, and defense. With the advancement of technology, better computing power, and the availability of large datasets containing valuable information, the use of AI and ML models has increased. The healthcare sector generates enormous amounts of data in terms of images, and patient data, which helps the healthcare companies to understand the patterns and make predictions. Artificial intelligence is capable of predicting acute critical illness with greater accuracy than the traditional early warning system (EWS), primarily used by healthcare providers.

AI, computer vision and machine learning systems proved that machines are better and faster than humans analyzing big data. Today, organizations have large datasets of patient data and insights about diseases through techniques like Genome Wide Association Studies (GWAS). Using AI, healthcare providers can analyze and interpret the available patient data more precisely for early diagnosis and better treatment. Today, it is possible to say whether a person has the chance to get cancer from a selfie using computer vision and machine learning to detect increased bilirubin levels in a person's sclera, the white part of the eye. As the interest in AI in the healthcare industry continues to grow, there are numerous current AI applications, and more use cases will emerge in the future.

Artificial intelligence (AI) initiatives are supposed to enable us to reinvent how we do business, and, one day, transform society at large. However, the truth is that most organizations are still barely scratching the surface of AI's potential. The biggest barrier to the success of AI continues to be an inability for many organizations to take advantage of the data they collect. Data is the lifeblood of AI; yet, according to an HBR survey, 69 percent of companies have not yet created a data-driven organization, and 52 percent aren't even treating data as a business asset. The bottom line is most companies haven't figured out how to break down their data silos and gain insight across all their data.

Buoyed by a $550M mega-round, funding for artificial intelligence companies in healthcare reached almost $1.6B in Q3'19. AI in healthcare companies peaked in both deals and dollars in Q3'19. Companies in the space raised almost $1.6B across 103 financing rounds in the third quarter, making it the top-funded sub-sector analyzed in CB Insights' latest Global Healthcare Report. Download the free report to find out more funding and investment trends from Q3'19. Leading the way for the quarter was Babylon Health, which raised a $550M mega-round from investors including the Public Investment Fund of Saudi Arabia, Munich Re Ventures, "a large US-based insurer" (speculated to be Centene), Kinnevik, and Vostok New Ventures.