Artificial Intelligence

The Machine Learning Future Is Now: How AI is Disrupting Entire Industries

Doctor using laptop with a stethoscope at their side
Credit: Elnur -

Machine learning and artificial intelligence (AI) are no longer the concepts of science fiction – they’re a $1.41 billion industry that is already making big changes to the way we understand and use immense databases for a wide range of purposes. From supporting cutting-edge cancer research to helping businesses track their inventory, machine learning and AI offer the ability to disrupt and enhance our existing processes in virtually every segment of society. 

The machine learning market is ready for lift off

The global AI space is expected to grow to $20 billion by 2025, according to research performed by Helomics. And it’s not just AI that offers growth opportunities – it’s also the disruption of long-standing industries that machine learning promises. By enabling business leaders to make more informed decisions, researchers to look at problems in new ways, and offering insights around the clock that no human could possibly contextualize alone, AI is one of humanity’s best allies in the future. 

It also carries with it immense market opportunity and the chance to catch the wave of the next big disruption. In fact, 86% of respondents in a 2021 PWC survey said AI technology is now a mainstream part of their company. More than 52% also reported accelerating adoption plans for machine learning and AI technology as a result of the COVID-19 pandemic and its impact on businesses and workplaces worldwide.

How AI and machine learning work

Without getting into the technical weeds, there are a few key capabilities that make machine learning a powerful tool. These include its ability to:

  • Contextualize vast databases quickly: “Big Data” is now a colloquial term because of how much information is generated, stored, and accessed in virtually every organization on the planet these days. Data is great, but it is only helpful if you can connect various data points and draw conclusions from them. The more data for this, the better…but there’s just one problem: Databases have grown so massive that no human could possibly sit down and parse all that information. It would take an entire lifetime just to scratch the surface of some of these datasets – but for machine learning it’s an easy and ongoing process.
  • Work around the clock: AI software doesn’t need sleep, so it can analyze data 24/7/365. That means even when your staff goes home, your machine learning algorithms can keep grinding away on current problems and offer up new insights by the time the next shift starts.
  • Improve and “learns” the more it works: Most machine learning algorithms are designed to improve at what they do as they comb through more data. At Predictive Oncology, for example, our Computational Research Engine (CoRETM) employs a polypharmacological/pharmacogenomic approach which builds a large set of predictive models and selects the optimal treatment plan. Coupled with the Helomics database of 150,000 deidentified patient records, 131 tumor types, and 30 types of cancer around the clock, CoRE is capable of comparing potential drug formulations to known patient responses in live treatment environments, giving cancer researchers and oncologists greater insight into optimal treatments based on every known factor. Best of all, CoRE can begin working with nothing more than two data points: one positive result and one negative result. Instead of relying on assumptions, like some other AI algorithms, CoRE is able to analyze any existing database, including those containing the lab results from Predictive Oncology’s research teams.
  • Apply to widespread applications: Machine learning and AI solutions are versatile like other types of software and can be designed for many different applications. For example, AI could be used to help monitor an organization’s network security, rapidly scanning connected devices to identify and flag any vulnerabilities before they’re exploited. It could also be used to identify a business’s opportunities for new cost-savings by streamlining existing processes and identifying waste in supply chains. There are virtually endless ways to apply AI to human life, from healthcare to business to entertainment – and that’s why the space is growing so quickly as the technology develops.

With its ability to support so many different types of needs and evaluate any kind of data – as well as compare it to other datasets – AI helps us make sense of all the data generated by our digital world.

Benefits of machine learning and AI

With these capabilities, it’s clear why you might want to have an AI on your side. Adoption of machine learning technology is being driven by improved sophistication of the tech, wider accessibility, and the benefits it offers. Some of the most commonly cited benefits across different types of organizations include:

  • Improved customer experience: According to the PWC survey, 86% of companies reported that AI helped them improve customer experiences. As a result, customer satisfaction and brand loyalty improved as well.
  • Informed decision-making: For 75% of business leaders, AI helped improve decision making and strategy at the top of the organization. That leads to better outcomes and competitive advantages.
  • Increased innovation: 75% of organizations who’ve fully implemented an AI solution said they innovated on existing products and services and were able to improve their offerings to better fit their target customers’ needs.
  • Cost savings: Streamlining processes and finding waste to cut is something AI excels at. 70% of organizations said their AI solutions helped them cut costs and save some precious capital.
  • Boosted productivity: Nearly two-thirds of organizations reported a productivity boost after implementing AI solutions. As a support system for personnel, AI makes peoples’ jobs easier and helps them do even more than ever before. With the right tools that fit for your team, it could even improve employee morale and retention.

In short order, AI is becoming a must-have for many businesses, and improved accessibility means machine learning is not just the realm of big business anymore. There's a good chance AI tools are even in some of the software you may use regularly – and understanding how an organization employs AI (or if it neglects it altogether) can be a pretty good indicator of its ability to realize its true potential. 

Leveraging AI in cancer research and predicting treatment outcomes

AI can improve cancer research by bringing patient data into early-stage, pre-clinical drug selection. Because AI can rapidly consider more factors about the cancer, the patient, and the treatment than humanly possible, it can help lead teams to make better decisions. This directly improves the ability of researchers, pharmaceutical companies, and oncologists to predict treatment outcomes more accurately than ever before.

That’s what Predictive Oncology’s solutions do. The Computational Research Engine (CoRETM) and patient-centric discovery platform PeDALTM are designed to do. These algorithms aren’t just smart, either – they also learn. The more CoRE and PeDAL are used in drug discovery and development, the more precise and effective they become at analyzing the data and supporting human decision-making.

Machine learning and drug development also go together in a way that reduces overall costs and improves the odds a company attains approval. In many cases, AI can streamline the entire process too, saving time in addition to reducing overhead. AI can predict how cancer cells become resistant to treatment and offers recommendations for adjusting formulations accordingly. 

By contextualizing data on known drug formulations, patient background and lifestyle, and various types of tumors and cancers, machine learning algorithms can easily identify the top drug formulations for specific types of cancer in defined patient populations – this vital information can be used in clinical trials, narrowing the error rate and improving clinical trial outcomes in oncology.

It can also be used to manage chemotherapy and support healthcare providers in developing optimal treatment plans. This can improve patient longevity and quality of life, as well as increase the odds that more patients survive through remission and into recovery. With AI by our sides the idea of eliminating cancer is not such a distant hope as we once dreamed. These tools offer us a new way to tackle one of the most complex diseases known to man and start winning the fight against cancer.

The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.