4 ways data and AI is transforming the healthcare sector
Artificial intelligence, or AI, has been accelerating innovation across the biomedical and pharmaceutical landscape in recent times, and that acceleration is well evidenced by the fact that just last month, a data-driven algorithm managed to come up with a plausible solution to combat antibiotic-resistant infection strains.
How did it do that? The algorithm managed to analyze over a hundred million different chemical compounds in a matter of days, in order to synthesize the new antibiotic, which the medical community is hailing as being able to eradicate at least 30 different strains of bacteria.
This type of application of big data and AI technology is not new or revolutionary, but rather it represents one of the many recent examples of AI tech application in the healthcare industry that could be herald a new golden age of discovery in medicine.
We take a look at some of the more pressing cases which are already beginning to shape not just healthcare, but how big data analysis can be perceived as a beneficial cause for changing lives.
# 1 | Tracking diseases on a global scale
AI researchers are applying machine learning strategies to track the recent spread of the coronavirus global pandemic, by analyzing web data, social media, and other metadata. Interestingly, this is not the first time big data was utilized to fight the spread of a deadly outbreak, as it was also called to task when the Ebola virus emerged back in 2014.
# 2 | Revolutionizing cancer diagnoses
Most clinical diagnoses of cancer still rely on manual testing techniques, many of which are surprisingly outmoded, subjective processes introduced over a hundred years ago. By the use of AI, some companies have seen rapid improvement in diagnosing and treating cancer, as now intricate data can be examined far more closely, and with a greater range of accuracy.
# 3 | Analyzing staffing needs
With healthcare institutions around the world constantly facing staffing and budgetary concerns, recent developments in turning AI technology towards predicting manpower requirements at different periods have found some success.
Now more than ever with a worldwide pandemic underway, healthcare providers such as the NHS Trusts in the UK can use historical data cross-referenced with patient numbers, and estimates of the virus spread movements, to determine when they need to scale up staff in time to deal with the outbreak in their region.
# 4 | Minimizing clinical variation
Recent data from the Institute for Healthcare Improvement indicates that the US healthcare system is the most expensive in the world, with a sizable chunk of that expense coming from clinical variation.
Clinical variation refers to the wrong usage and wastage of healthcare services and resources. To lower variation costs would require AI tech to analyze huge quantities of data, which is entirely possible with the currently available technology.
Hospitals, healthcare trusts, and other health research facilities need to gear up for this transition into a data-driven tomorrow, as artificial intelligence advances in this regard are only made possible by the mountains of gathered data, which will require solid IT infrastructure and sizeable computing power to effectively analyze the information within the required timeframes.