AI in medicine needs more data to reach its full transformative potential.

Cerebrospinal fluid leaks are a rare occurrence, caused by small tears or holes in the spinal cord. These leaks can be extremely difficult to identify, as the symptoms they cause are not uncommon and can be easily mistaken for other ailments. Patients often experience nausea, neck pain, ringing in the ears, and debilitating headaches that are worsened by certain positions. It's not uncommon for these individuals to spend years searching for a proper diagnosis, with some even being told that their symptoms are caused by allergies.

But as with many medical fields, the rise of artificial intelligence (AI) is poised to revolutionize the way these conditions are detected and treated. By utilizing advanced technology and algorithms, AI has the potential to improve accuracy, save money, and drastically improve the lives of patients. While most AI-enabled devices currently approved by U.S. regulators are used for diagnostic purposes, the possibilities for AI in healthcare are vast. From streamlining administrative tasks to accelerating drug discovery, the adoption of AI could potentially save up to $360 billion in annual health spending.

One area where AI is already making a significant impact is in the detection of spinal fluid leaks. While traditional MRI scans can show changes in the brain that may suggest a leak, finding the source of the leak itself requires a higher level of detail that can only be provided by a CT scan. However, the technology behind these scans has not seen significant improvements in decades. That is, until now.

Thanks to the integration of AI and advanced semiconductors, a new type of CT scanner known as a photon-counting CT is able to detect even the smallest of spinal leaks that were previously invisible. This breakthrough technology has allowed for more accurate diagnosis and treatment, leading to full recovery for many patients. Some have even described it as life-changing.

But the potential uses of AI in healthcare go far beyond just neurology. These advanced scanners are also able to identify small irregularities in the body before they become major health threats, such as unruptured aneurysms or dangerous levels of arterial plaque. This has the potential to revolutionize preventative care, particularly for cardiovascular disease and stroke, which are two of the top causes of death globally.

However, one of the biggest challenges for AI-reliant medical devices is the amount of data needed to train their models. In the U.S., this information is often siloed in different provider and hospital databases, making it difficult for innovators to gain access to the necessary data. Despite the government's efforts to encourage data sharing, over 60% of hospitals reported facing barriers to information exchanges last year, with many still relying on outdated methods like fax machines.

In order for AI to reach its full potential in healthcare, this issue needs to be addressed. The accuracy and usefulness of AI models rely heavily on access to vast amounts of data, ideally from multiple health systems and countries, in various formats and languages. While the private sector is already working on developing AI tools that can process unstructured data, there is still a lack of support from U.S. health agencies for these products. This is a crucial first step towards wider adoption.

Lawmakers can also play a role in promoting the use of AI in healthcare. By providing funding, agencies can collaborate to create a large dataset of high-quality, anonymized patient information that can be used to train AI models. This "regulatory grade data" would not only improve diagnostic accuracy, but could also lead to more streamlined approvals for AI-driven treatments. Just last week, the FDA and Department of Veterans Affairs announced a joint effort to test AI tools using VA data, showing promising progress in this direction. Additionally, narrowing the FDA's mission to focus on ensuring data quality and preventing bias could make better use of limited resources.

Of course, relying on AI for diagnoses and preventative screenings comes with its own set of challenges. There is a risk of unnecessary or even harmful interventions, and cost may also be a barrier for some individuals. However, as technology advances and becomes more affordable, access to faster and more accurate scans will increase, especially for high-risk patients. It's worth noting that the initial skepticism towards preventative CT scans for smokers quickly dissipated once research showed that they drastically reduced the risk of lung cancer.

The potential for AI to significantly improve the lives of patients is no longer just theoretical. With increased access to data and advancements in technology, AI-driven treatment has the potential to become the new standard of care. It's an exciting time for the medical field, and the possibilities for AI in healthcare are endless.

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