Use of AI for Medtech
Use of AI for Medtech - Drug Development and Discovery
Artificial Intelligence is in no doubt revolutionizing the health care sector. Investment in AI by major health care organizations has drastically grown in the last two years. According to recent research by Deloitte, 75% of major organizations grossing over $10 billion in revenue invest over $50 million in AI annually.
AI is used increasingly as an analytical tool to help make decisions about healthcare and drug development research and as an integral part of the process itself. Currently, AI in healthcare is emerging as a notable line of defense due to the covid-19 pandemic.
According to a report by Global Market Insights, Inc, the healthcare AI market is estimated to grow at a CAGR of 46.3% and attain over $21 billion in valuation by 2026.
The advent of artificial intelligence in the healthcare sector could yield significant changes that impact nearly every aspect of patient care. AI has the potential for great promise across all medical disciplines, including surgery, drug discovery, and development.
How Artificial Intelligence is Redefining Medical Technology
With a lot of medical data becoming increasingly manageable and measurable, AI is making it easier to process and analyze patterns in medical data. Machine learning (ML) integration into the health-tech segment has tremendous potential for transforming traditional methods like managing diseases or diagnosing them accurately.
Recognizing the potential of AI in the medical industry, more than 82% of MedTech leaders have indicated that its integration into their business is crucial to stay ahead. This puts MedTech far ahead of others, such as consumer goods or software firms, in terms of AI adoption.
Artificial Intelligence in Drug Development and Discovery
AI has helped revolutionize the drug discovery process and opens new avenues for scientists in drug development. The invention of AI-powered systems has allowed researchers to complete preliminary clinical trials much faster and in an efficient way.
Artificial Intelligence can help new drugs reach the clinical trial stage five times faster while reducing industry costs by almost 30%. At present, it takes nearly a decade for medications to get approval from regulators, with an approval rate of less than 12%. This process can cost pharma companies $2.6 billion on average, according to findings by Tufts Center for the Study of Drug Development's Journal.
DSP-1181, a joint venture between UK-based Exscientia and Japan-based Sumitomo Dainippon Pharma, has entered Phase I clinical trials. The drug is the first in history to use artificial intelligence for the development of new medicines. Using AI allowed researchers to complete each stage much quicker than usual. The same research that would have taken nearly five years using conventional discovery took less than 12 months.
Risk and Opportunities Associated With Using AI in the MedTech Sector
While Artificial Intelligence has tremendous potential to empower the medical industry, it is important to understand that Artificial Intelligence can only deliver its best results when used with human intelligence. Here are the opportunities and risks associated with AI in health care:
Opportunities
Increased Accuracy
The most common use of Artificial Intelligence in the health care industry is to increase accuracy. According to a study by John Hopkins University, more than 250,000 annual deaths in the U.S are due to medical errors.
AI can help doctors make more accurate and precise diagnoses, monitor patient health more closely, and even predict life-threatening events before they happen. AI platforms use algorithms to determine whether a drug is suitable based on the patient profile.
Increased Efficiency
According to research by Deloitte, AI applications can free up 1,659 million - 1,944 million hours every year, improving efficiency in the Medtech industry. These applications include health assistance (VHA) and robotics.
Artificial intelligence (AI) can reduce the time hospitals spend on staff scheduling by up to 90%, comparable to manual scheduling solutions. According to Deloitte, one of the biggest challenges in health care organizations is staff scheduling, which AI could help address by enabling improved management and alerts about any potential issues with staffing levels before they arise.
Accelerated Scientific Discovery
As witnessed with the rapid development and manufacturing of the Covid-19 vaccine, collaboration is vital to scientific development. Data exchange and collaboration enable AI to tap into patients' global datasets for pattern recognition that scientists are yet to notice. Artificial Intelligence is also improving scientific discovery by creating predictive models to help identify new biomarkers to make clinical research more precise and effective.
Risks
Skepticism in AI Integration
AI can be a powerful tool in the health industry, but it's not a replacement for doctors. Despite advancements in AI systems and their ability to diagnose patients with impressive accuracy, they are not yet perfect. AI needs experienced physicians to diagnose patients concurrently.
Organizations also need clear guidelines on training staff members about how an AI system functions and who should handle cases where the technology itself may have made errors.
AI Training
When designing an AI system, cultural biases must be considered to ensure input data is accurate. It's essential for systems designers and developers of these artificial intelligence programs to remember who they are creating the technology for; if not, misdiagnoses may occur.
AI algorithms used by medical professionals should be audited regularly to eliminate bias. Auditing practices should aim to improve accuracy while guarding against harmful cognitive fallacies present in human thought processes.
Ethical Data Issues
While technology is great, it's essential to consider the ethical implications. As AI becomes more and more prevalent in medicine, we need to ensure that patients are fully aware of data usage to give informed consent.
Key Takeaway
AI has the potential to be a game-changer in drug discovery. The technology can analyze unstructured data and provide actionable insights at an unprecedented speed, thus accelerating medical research by years if not decades.
AI is being used for high accuracy analysis of large datasets traditionally too cumbersome or laborious to examine effectively with traditional methods alone. This increased efficiency will also allow researchers more time and opportunities to explore new avenues, leading them down paths they would never have thought possible before due to their limitations on time or resources available.