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.

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