Artificial intelligence (AI) is transforming the landscape of virtually every industry. Where does the still-wet-behind-the-ears technology fit in with the centuries-old practice of lifesaving?
From spotting cancer early to designing lifesaving drugs, AI's potential to help people live longer, healthier lives is enormous. But while medicine is one of the sectors that stands to gain the most, healthcare remains "below average" in AI adoption compared to other industries, according to data from the World Economic Forum. Healthcare must overcome significant challenges to harness AI’s potential while ensuring systems are accurate, bias-free, and safe.
Ahead, we discuss AI's wide-reaching impact on the healthcare world, including benefits and challenges.
How is artificial intelligence used in medicine?
Already, AI has shown progress in areas like diagnostics, predictive analysis, and drug discovery. Here are a few of the ways artificial intelligence is used in healthcare:
Medical imaging & diagnostics
AI's potential in medical imaging and diagnostics goes way beyond automation. The tech is fundamentally changing disease diagnosis, making it more efficient and precise.
Integrating sophisticated algorithms and machine learning, AI can accelerate medical image analysis, dramatically reducing the time it takes to diagnose a patient. AI also improves the accuracy of diagnoses. Pulling from vast datasets of medical images, deep learning models can identify complex patterns and anomalies that are difficult to detect by the human eye. Faster, speedier medical imaging and diagnostics translate to earlier interventions, improved patient outcomes, and saved lives.
Predictive analytics
The ability to predict patient outcomes more accurately is a perpetual game of catch-up. AI-powered predictive analytics can be a game-changer, helping providers forecast diseases, anticipate staffing and resource needs, and inform clinical decisions. AI models leverage big data to identify patients at risk of developing certain conditions, enabling earlier and more effective treatments.
Personalized treatment plans
AI models can process a high volume of data in a fraction of the time as a handful of human counterparts. Treatment paths that require intensive monitoring from healthcare providers, such as sleep studies, microbiome studies, and neurological disease tracking can benefit from using AI to sift through the mountains of data collected. When combing through the months or even years of data from one of these studies, sharply trained algorithms can potentially identify the presence of underlying conditions that may go unnoticed by the human eye.
Drug discovery & development
In a century of modern medicine, humanity has identified thousands of diseases, developed treatments for a few hundred, and eradicated just two (smallpox and rinderpest). The most precious commodity in the drug development world is time — time to study, time to test on cells & tissues, time to test on humans. Pieces of that timeline can be accelerated by running thousands of simulations in the same period it would take to observe one real-time trial. AI can take all the data ever published in the realm of immunological research and potentially reduce time spent on the path to human trials.
AI in surgery & robotics
Robots have been in our operating rooms for decades, assisting surgeons from incision to stitch. By integrating AI into robotic systems, the industry is poised for further transformation.
AI may not be able to hold the scalpel, but it can be part of an interconnected force between the machine assistants in the operating room and the human surgeon behind the wheel. With the ability to monitor patient vital signs and alert surgeons in real-time, the human-AI-robot trinity could soon be the standard setup for every OR.
Administrative workflow automation
Not to be overlooked are the benefits of AI on administrative workflow. Natural language processing and machine learning algorithms automate processes like scheduling, billing, and medical documentation. By taking over routine tasks, AI frees up clinicians for higher-level activities and face-to-face time with patients.
Challenges of AI in medicine
Healthcare providers and patients both stand to benefit from the integration of generative artificial intelligence and machine learning tools. While AI opens up a world of possibilities in the medical field, the new tech also brings risks. Here are the challenges of AI in medicine:
Data privacy
Healthcare data, which often includes personal details and medical history, is inherently sensitive. The industry operates under strict data protection regulations like the Health Insurance Portability and Accountability Act (HIPAA), raising compliance and security concerns.
Ethical concerns
AI algorithms can inherit existing biases from training data. Powered by discriminatory data, AI models could perpetuate or even amplify disparities in healthcare outcomes among certain patient groups.
Adoption barriers
As noted, AI adoption in medicine has been slow compared to other industries. Some of the biggest stumbling blocks to AI implementation in the medical sector include:
- Regulatory barriers. Existing rules are designed for fixed healthcare systems, while AI is fluid and changing rapidly. Regulation is key for unlocking AI’s full potential while protecting patient safety and rights.
- Trust. Healthcare professionals may hesitate to adopt AI technologies due to concerns about reliability, loss of control, and the perceived threat to their roles.
- Infrastructure limitations. AI integration requires significant investment in technological infrastructure, regulatory compliance, and staff training.
Talent shortage
Designing and managing AI solutions in medicine requires specialized technical expertise in machine learning, data science, and software engineering. Tech workers are already in high demand, and skilled professionals with the knowledge and experience needed to design, implement, and maintain AI-driven systems in healthcare settings are even more so.
The growing need for specialized engineers
As AI-empowered technology grows into a foundational cornerstone of the industry, medtech companies need capable engineers to keep current with the evolving landscape. At Howdy.com, we understand the many applications of artificial intelligence in medicine and see the writing on the wall regarding the future of every industry: AI is changing absolutely everything.
Nearshoring engineers with Howdy can be a cost-effective way to scale businesses, especially when the engineers are AI-capable. Howdy’s rigorous selection process nets the top 1% of engineers, so it’s not just about getting access to great AI talent, it’s about creating long-term business solutions in a constantly changing tech world.
Conclusion
There are new applications for AI in medicine virtually every day, with countless new ones waiting to be discovered. As the training data, statistical accuracy, and overarching ethics of AI improve, the necessity to adapt to an AI-empowered workforce grows daily. Medtech companies in particular, due to the sensitive information they handle and the personal touch needed for all healthcare, can benefit greatly from an AI-enabled team.
Curious about what an AI-trained team can do for your business? Book a demo to chat with one of Howdy.com's industry experts.
