Life Sciences

3 Ways Artificial Intelligence Can Help Medtech Save Lives

Nowhere in healthcare is artificial intelligence (AI) making a more immediate impact on patient lives than in medical technology. From advanced diagnostics and imaging to enhanced surgical procedures, here’s how AI is helping improve patient outcomes for all.

Nowhere in healthcare is artificial intelligence (AI) making a more immediate impact on patient lives than in medical technology. From advanced diagnostics and imaging to enhanced surgical procedures, here’s how AI is helping improve patient outcomes for all.

Artificial intelligence (AI) is generating a great deal of buzz for its potential to transform healthcare, and the medical technology industry sits at the forefront of this effort. AI is pushing medical practitioners to rethink how humans and machines can work together to improve the development and delivery of healthcare.

While technological and regulatory hurdles remain, companies and providers are embracing a future where AI makes a significant difference in the quality of patient care. Today, AI is improving medtech in three key ways.

1. Improving Diagnostic Accuracy

One of the more advanced areas of AI integration is already starting to benefit patients today. Diagnostic errors can lead to serious complications and even death1, but AI has the potential to help diagnose and treat diseases more accurately.

In pathology labs, for example, AI tools can help to better identify cancer types and stages of development. Pathologists can draw on AI’s ability to cross-reference hundreds of thousands of comparable slides and data sets to both predict and evaluate different diagnoses. Cancers can be identified more accurately and efficiently, and at earlier stages of progression—ultimately leading to better treatment protocols and patient outcomes.

Like pathology, medical imaging is an aspect of healthcare that has traditionally relied on human interpretation alone, but the immense volume of imaging presents an opportunity for more efficient analysis. Medical imaging—such as MRIs and CTs—essentially provide a tool for radiologists to identify underlying patterns. Since pattern analysis and recognition sit at the core of how AI is built, AI can be used by doctors in all facets of the decision process—from detection and diagnosis to decision analysis and treatment. AI can simply cross-compare and analyze a more complete data set than humans can assess by themselves. This can improve both the quality and speed of the analysis, benefiting the efficiency and cost for doctors and the health system as a whole. 

2. Achieving More Precision in Surgery

The use of AI in the operating room has been understandably slower to take hold. This is partially due to more complex regulatory hurdles, but the technology itself is also much more complex to apply in a surgical setting.

The primary use of AI in the operating room today revolves around surgical assistants, or resources that doctors use to improve efficiency, reduce variability and improve surgical outcomes. AI can provide real-time data points on a patient’s status or the surgeon’s own movements, assisting the doctor throughout a complex surgery. This provides more visibility into data so that surgeons can improve their performance both throughout the procedure and as a follow-up analysis for future procedures.

AI is also used to enhance surgery in areas where humans simply can’t perform at microscopic levels, such as microsurgeries on blood vessels and other tissues where human movements are translated to more precise, minute actions with robotic assistance.

3. Providing Better Patient Monitoring

Consumers are already used to their phones and other devices gathering significant amounts of information on their lives and habits; now, using this information to better manage personal health is gaining in popularity.

While there remains understandable public skepticism and barriers around privacy and use of data, the benefits to patients and their doctors can’t be ignored. From proactive care to ongoing disease management, the use of AI to collect, analyze and interpret real-time patient data is extremely valuable. Doctors benefit from greater visibility into data and behaviors that can improve outcomes, and patients benefit from taking a more active role in the management of their health and wellbeing.

All of this can also be done outside the hospital setting, which may help reduce the cost and strain on the system. Whether through the use of standalone devices or integration with existing phones, watches or other personal devices, remote patient monitoring and use of AI in the management of individual health is on the rise.

Regulatory Challenges Ahead

Like software and informational technology generally, the use of AI in novel medical technologies has historically suffered from lagging regulatory guidance and approval pathways. The US government has made strides in alleviating the issue over the past decade, such as by providing further guidance in the 21st Century Cures Act and the more recent Digital Health Innovation Action Plan, but AI is still an evolving area of healthcare. Several recent devices that utilize AI were granted FDA approval through the de novo pathway, offering some hope to manufacturers that quicker, more streamlined approvals could be on the horizon.

But the industry will continue to struggle with more macro issues, such as privacy in data management, lack of precedent on health benefits and the inherent need for continued improvement and reinvention of AI algorithms that necessitate further regulatory submission and approval. The medtech industry is well aware of the challenges, however, and is working vigorously with regulators to provide continued guidance and improvements to encourage further technological adoption of AI.

 

Peter Meath, Industry Head, Middle Market Life Sciences Banking

Peter Meath

Peter Meath, Industry Head, Middle Market Life Sciences Banking

Peter Meath serves as the Managing Director and Life Sciences Industry Head for Middle Market Banking & Specialized Industries. Prior to joining J.P. Morgan, he was a co-founder of Square 1 Bank, the second-largest de novo bank startup in US history, and only the second bank in the US to solely focus on emerging growth technology, healthcare and life sciences companies.

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