The life sciences are no strangers to technology – despite being concerned with the natural processes of life, the industry has pioneered the use of artificial intelligence (AI) since the 1960s. The problem-solving program DENDRAL was first used in organic chemistry during this decade, paving the way for MYCIN, one of the earliest and most significant uses of AI in healthcare. More advanced programs followed, which, owing to their complexity, never quite took off.
Fast forward to the present day, and the rapid advancement of machine learning (ML) technologies is having a critical impact on the life sciences. Big Data, the Internet of Things (IoT), and advanced analytics act as supports to ML technology and are simultaneously driving the market forwards. AI and ML are set to transform numerous industries, and by association, our entire way of life. But it’s the life sciences that are at the heart of this change.
BCC Research has predicted that the global market for machine learning in the life sciences will reach as much as $20.7 billion by 2027, growing at a mammoth rate of 45.5% up until 2027.
Machine learning in diagnosis
Machine learning is having a transformative effect on healthcare. In the field of medical diagnostics, ML and cancer diagnostics are intersecting. The result is an enormous increase in the potential to save lives.
Cancer diagnosis and staging rely heavily on pathology tests, which determine where the cancer is and whether it has spread. Machine learning algorithms can be trained to improve numerous areas of diagnosis, including making the process faster, more detailed, and more precise.
With the help of machine learning, AI can identify tumors from cancer imaging. This alone has a myriad of positive effects. Even the most experienced doctors dispute whether a given mass is cancerous – and less experienced professionals are even more likely to be uncertain. AI can provide another insight into diagnosis. This leaves radiologists with the far less time-consuming task of determining whether the AI has made an accurate assessment. The potential for ML to free up the time of healthcare professionals is significant. Given that the American Cancer Society estimates the global cancer burden to be 21 million by 2030, the assistance could not come at a better time.
Machine learning in improving therapy
The Covid-19 pandemic had a snowstorm of effects on people globally. As well as physical complications, the mental strain on populations has been significant. People are increasingly seeking help to deal with the fallout of the pandemic, leaving healthcare providers searching for new ways to cope with demand. AI and machine learning have the potential to create a 180 on the way we identify and treat mental health disorders. Natural-language processing (NLP) technology can examine the language used in therapy sessions and identify the most effective phrasing and utterances used in treatments. This groundbreaking technology can help pave the way for more individualized mental healthcare and enable customized psychiatric therapies – which is crucial for prescribing medicines.
Machine learning in healthcare management systems
Despite significant advancements in the computerization and digitalization of medicine, effective and reliable management tools are yet to be developed. Conventional management systems do not scratch the surface of the enormous complexity and variety of healthcare operations. While this is a factor that is unlikely to go away, machine learning algorithms can do a lot of legwork in overcoming these issues. ML can create robust models from weakly predictive information and locate important aspects in complicated feature sets. These two fundamental benefits will begin to address the weak spots of healthcare management systems.
Slow adoption rates
As with any industry, challenges remain for the adoption of ML within the life sciences. Although AI has established itself as an important component of the industry, many practitioners are surprisingly reluctant to embrace the technology. A lack of data to support healthcare decisions is creating a low adoption rate and what’s more, these technologies are hugely expensive. Significant initial investments are required, plus the ongoing costs of maintenance and repair.
And while in some ways ML can increase security, systems are also at risk of hacking. Cybercriminals can infiltrate systems and build their own ML to be sold on the “dark web”. These hackers are effectively weaponizing machine learning systems against themselves. In a 2017 paper titled “Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN”, the first known use of ML in malware creation was presented. The authors revealed how they built a generative adversarial network (GAN) based algorithm to generate adversarial malware samples. These malware samples were able to critically bypass machine learning-based detection systems. They were able to fight fire with fire.
Stories such as these can serve as the icing on the cake in putting off medical practitioners from adopting ML technologies. As well as being risky, they’re complex machines and expensive.
Decode machine learning within the life sciences
BCC Research’s global market report on Machine Learning in the Life Sciences determines specific applications and provides five-year forecasting for the industry. The report examines current and future industry scenarios which, considering the astronomic growth rates, are invaluable to those with vested interests within the industry.
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