AI: Revolutionizing Healthcare with Enhanced Diagnostics and Personalized Interventions

Although the medical community has seen numerous advances in medical imaging and other diagnostic technologies, errors in diagnosis are still rampant. One recent National Library of Medicine (NLM) study estimated that diagnostic errors affect five percent of all U.S. outpatients and are responsible for up to 17 percent of all adverse events in hospitals.

Artificial intelligence tools, however, can help reverse that trend by augmenting the effectiveness of human clinicians and improving diagnostic accuracy. Big data and AI algorithms can even make health interventions more personalized and effective.

Let’s examine how in more detail below. 

What Promise Does AI Hold For Diagnostics and Personalized Interventions?

While AI systems have clear limitations, so do (often overworked) clinicians: Most physicians work between 40 and 60 hours per week, with one-quarter of U.S. doctors working up to 80 hours a week. Many nurses routinely work 16-plus-hour days. 

Diagnostics

Given such an environment, it’s unsurprising that diagnostic mistakes can and do happen. The promise of AI is that it can help augment and scale the effectiveness of health workers when diagnosing complex ailments within such a busy environment. 

Predictive AI models can help doctors detect patterns from medical images such as CT and MRI scans and other data to predict or diagnose disease. And generative AI models trained on large medical datasets are increasingly being used to suggest diagnoses or alert physicians to trends the latter may not spot on their own.

Although not yet widespread in the healthcare industry, the steady improvement of generative models means their use will likely increase. A recent Stanford study found that OpenAI’s most recent GPT iteration, GPT 4, outperformed first- and second-year medical students on complex clinical care exam questions. The model also outperformed its predecessor, GPT 3.5, in the same exercise. 

OpenAI’s official position, however, is that neither model is fine-tuned enough to be used as a standalone diagnostic tool for complex diseases. Always visit your doctor – and don’t just log into ChatGPT – if you’re experiencing a potential medical issue. 

Personalized Health Interventions

Traditional “one-size-fits-all” health interventions – where the treatment approach is more or less the same for every patient – for complex diseases such as cancer have increasingly fallen out of favor as personalized or precision medicine (PM) approaches become more common. And AI has a lot to do with that.

Natural language processing (NLP), a type of machine learning (ML), can ingest and make sense of medical records, doctors’ notes, social media data, conversation transcripts, and other data points in seconds by rendering unstructured data (such as text) into numeric representations. It can examine these large datasets to detect relevant trends orders of magnitude faster than any human.

While tailored medical treatment based on individual needs is certainly nothing new, precision health interventions based on multiple large data sources simply weren’t possible without modern big data and AI/ML tools. 

And indicators show the use of AI and NLP techniques is on the rise: A recent systematic review and meta-analysis of scientific papers on mental health interventions (MHI) showed that more than 50 percent of all MHI studies mentioning NLP-related techniques were published between 2020 and 2022. That suggests “a surge in NLP-based methods for MHI applications,” according to News Medical. 

How AI Helps With Diagnosis

Diagnosing a relatively common ailment like a broken arm is pretty straightforward. But it’s much more difficult for more complex, rare, or less predictable conditions that can manifest differently in different individuals, such as Multiple sclerosis (MS).

AI can help by analyzing thousands of images or other data points to spot anomalies or differentiate between healthy and diseased tissue. Because conditions such as heart disease and cancer change the physical behavior of tissue, experts can train algorithms to detect such differences – even if they’re so minute that the human eye can’t spot them.

“It’s that component of machine learning that allows us to identify disease before it can be spotted by trained physicians,” says Dr. Mark Traill, director of Medical Imaging AI Projects at the University of Michigan Health-West, in Elevance Health. 

He explains that AI models can also help predict disease with surprising accuracy. “Already, we’re using AI risk algorithms to go deeper into a standard 3D mammogram and identify patterns that suggest a person is at risk of developing an aggressive breast cancer over the next 12 months,” he says, adding that these algorithms often outperform trained radiologists. 

Traditional screening mammograms miss around 20 percent of breast cancers, after all. And because missed diagnoses inevitably lead to delayed treatment, the results of such diagnostic mistakes can be catastrophic for patients. 

How AI Helps With Personalized/Precision Medicine

The millions of data points generated and collected by modern medical devices and monitoring apps have changed how physicians can manage an individual’s health. They’ve also changed how physicians deliver the proper health intervention at the correct time, according to Executives for Health Innovation.

As mentioned earlier, the concept of personalized interventions is nothing new. It dates back to the ancient Greek physician Hippocrates (4th century BCE), who evaluated data points such as patient age, physical appearance, and even the time of year when prescribing medicines.  

Modern personalized interventions use more sophisticated data from medical devices, electronic health records (EHRs), and genetic information for improved therapeutic targeting and other treatments. 

Health professionals can also use patient preference data around side effects and medication delivery to ensure better overall patient satisfaction.

And when dealing with complex diseases involving several different medications, such as epilepsy or MS, AI can determine which drugs may work best for which people. The traditional alternative is to conduct trial-and-error on actual patients, which carries inherent risk and can take months or even years to determine the best treatment.

The advent of big data analytics and AI has made harnessing insights from these datasets possible. 

Examples of AI-enhanced diagnostics

Plenty of examples exist that demonstrate the promise of AI-powered diagnostics. Here are just a few of its potential applications:

Lung cancer: A deep-learning algorithm outperformed six trained radiologists in detecting lung cancer tumors in a study of more than 42,000 low-dose computed tomography scans (LDCTs), detecting fewer false positives and negatives. 

Pancreatic cancer: A proof-of-concept study by Johns Hopkins’ Sidney Kimmel Comprehensive Cancer Center involving the ML tool CompCyst outperformed current clinical management in identifying patients with a low risk of malignancy (60% vs. 19%).

Brain and central nervous system (CNS) cancer: Sturgeon, an AI model developed by researchers from the Oncode Institute, Center for Molecular Medicine, and other organizations, could accurately diagnose brain tumors within 40 minutes up to 90 percent of the time. Traditional tumor identification is often much more time-consuming and can be inaccurate.

Eye diseases: A new retinal image foundational AI model, RETFound, can recognize signs of eye diseases in retinal images to help enhance diagnostic effectiveness. RETFound is a self-supervised learning (SSL) masked autoencoder-based foundation model trained on 1.6 million unlabeled retinal images.

Further studies have shown that multimodal AI models, or models that draw on more than one data type, can be even more effective for diagnosis than models that only use images alone. 

For example, one multimodal model developed by University Hospital Aachen that uses clinical data combined with images to diagnose 25 different diseases showed a 77 percent accuracy rate. That’s compared to 70 percent for models that used only images and 72 percent for models that used only clinical data.   

Challenges For AI-Enhanced Diagnostics and Interventions

While AI shows excellent promise in diagnostics and personalized health interventions, several challenges remain. 

Critics often cite patient privacy issues to account for the lack of large amounts of training data for AI models in healthcare. Tools now exist to automate the removal of personal health information (PHI) from large datasets, but privacy in healthcare data remains an issue.

Additional roadblocks facing the widespread adoption of AI in diagnostics and health interventions include:

• A lack of trust: It’s a big leap for both patients and physicians to put their faith entirely in the hands of technology when dealing with a life-or-death issue. That’s why even the most ardent AI advocates say physicians shouldn’t entirely rely on AI-based diagnosis, and instead should treat it as just another tool in the clinician’s toolbox.
  

• A lack of time: Most physicians don’t have the time to implement another tool into their workflow. AI tools need to integrate with current workflows to spur widespread adoption, similar to how the Mayo Clinic embedded its new algorithm to spot atrial fibrillation within the clinic’s EHRs, which the doctors use anyway.
  
• A lack of humanity: ChatGPT talks a great game. But AI models can’t compete with people regarding bedside manner, when making subtle observations of patient behavior, or when listening for gaps in a patient’s backstory. 

• Data quality: Data quality issues in healthcare are often related to the privacy issue mentioned above since some datasets simply can’t be used due to privacy concerns. But other issues include generative AI hallucinations, which is when large language models (LLMs) produce inaccurate or even nonsensical outputs.

• AI bias: A growing concern in the industry is that AI models can be racially biased based on their training data.

Conclusion

Despite these challenges, AI shows tremendous promise in helping to scale and improve diagnoses and personal interventions when combined with knowledgeable health professionals. 

AI and big data can improve the accuracy of diagnosing complex and rare diseases. These technologies can also help tailor health interventions – such as prescribing certain medicines to treat diseases – based on patterns in large amounts of personal health data that often go unrecognized.

But to implement these technologies effectively and responsibly, healthcare organizations need the right technology partner. CapeStart works with healthcare researchers and organizations worldwide to improve the quality of care and patient experience while lowering delivery costs through AI and data-driven healthcare. 

Contact CapeStart today to schedule a one-on-one discovery call with our AI, ML, and data experts.