The relationship between a person and their healthcare provider is sacred. Examining, diagnosing, planning, and bedside manner make up the foundation of a good patient experience.
For years now, doctors have relied on technology to assist with their procedures. Electronic health records, remote patient monitoring, telehealth visits, and robotic surgeries are just a few examples of the significant progress made. You probably even see doctors carrying around tablets that give them instant access to decision-making algorithms based on your systems and personal history.
That progress, however, is about to speed up beyond our wildest projections with the advent of AI tools. This set of innovations was first introduced in 2014 via IBM Watson Health, a range of solutions that leveraged data analytics to improve patient care, advance medical research, and enhance healthcare management.
Since then, we have seen an explosion in the generative AI tool market, with many companies anxiously trying to push our healthcare system into the future.
Revolutionizing Diagnostics & Treatment
One of the first major impacts of the inclusion of AI in medical procedures can be observed in radiology. AI-driven algorithms are revolutionizing the interpretation of images, diagnostic accuracy, and quality of patient care. MRIs, mammograms, CT scans, and X-rays are all being examined at a speed and accuracy that surpasses human capabilities, allowing professionals to prioritize cases and improve outcomes.
In pathology, machine learning techniques leverage large amounts of histopathological data, enabling doctors to make accurate and timely assessments. AI-driven tools have already proven useful in the identification of cancerous tissue and the prediction of disease progression.
Even surgical robots, which first began to appear in the 1980s, are leveraging AI to enhance precision, efficiency, and safety during surgical procedures. They use advanced imaging, sensing, and machine-learning techniques to navigate complex anatomical structures and reduce the risk of complications.
Accessibility & Efficiency
Virtual health assistants (chatbots) leverage natural language processing to provide personalized guidance and support by way of answering a patient’s healthcare-related questions and offering tailored reccomendations. This is particularly crucial in underserved areas where medical care is scarce.
Hospital operations are experiencing a major benefit from these tools through the reduced burden of paperwork. AI-powered solutions are automating mundane tasks such as scheduling, billing, and claims processing. They can also utilize these algorithms to predict patient admission rates, identify bottlenecks in administrative workflows, and optimize resource allocation to ensure the hospital is running at peak efficiency.
Patient Care & Safety
Perhaps the most prioritized benefit of AI involves the possibilities in the realm of patient care and safety. A 2016 study by Johns Hopkins University found that medical error contributed to more than 250,000 deaths per year, making it the third-leading cause of death in the United States.
To mitigate these statistics, many companies are offering advanced solutions that will assist in clinical decision-making and monitoring patient vital signs. For instance, the Philips eICU program uses AI algorithms to track patient vitals and alert professionals to issues before they become life-threatening.
Medication management and drug administration are also set to see an efficiency shift as more tools become available that provide real-time guidance on dosage adjustments and drug interactions.
Challenges and Ethical Considerations
As with all great innovation, this does not come without a collection of challenges and concerns.
One initial concern is the potential for over-reliance, which could undermine the professional judgment of healthcare providers. After being educated and trained for more than a decade, many feel it would be unwise to minimize physicians’ expertise.
When it comes to the actual function of the tools, the infancy of the technology’s use in all industries raises many concerns about the reliability and accuracy of AI algorithms. A cursory examination leads some to believe that inherent biases could lead to errors in diagnosis and treatment recommendations.
These algorithms also require a wealth of patient data to analyze before its predictive benefits can be realized. With more than 500 million personal healthcare records being exposed via data breaches between 2009 and 2023, there is a reasonable fear that there is not enough consideration being shown for data security.
Forecasting the Future
Overall, the healthcare industry is notoriously slow to integrate cutting-edge technology. Delays in the regulatory landscape have slowed down the adoption of AI and ensured that the proper precautions will be taken before these tools are available in your local hospital.
Not everyone is waiting to move forward. Certain systems, such as the Mayo Clinic and the Cleveland Clinic, have already adopted some of these software platforms. The UK government has allocated a £13m investment focused on advanced AI research within healthcare. Hackensack Meridian Health CEO Robert Garrett spoke boldly at a recent HIMSS global conference, stating, “We must build the health system of the future.”
Without question, machine learning algorithms are not going anywhere. Their impact on personalization, efficiency, and cost are far too appealing to allow professionals to label this as a fly-by-night technology trend.
Balancing innovation and efficiency with the indispensable quality of the human touch still proves to be a large challenge waiting on the horizon, and we may soon be required to examine how comfortable we are with a machine making medical decisions.
As we stand at the precipice of what feels like a healthcare revolution, the applications of this burgeoning trend seem boundless. This should fill everyone, especially opponents of the current healthcare system, with a great sense of optimism.
However, it is important to remember that we are not pioneers – we are patients – and a large amount of patience should used while these tools migrate into our care.