Machine Learning-based AI Systems are Helping Doctors Diagnose Diseases

AUSTIN, Texas - Aug. 22, 2024 - PRLog -- AI-based medical systems are helping healthcare providers improve the care they offer to patients.

For example, AI-based genetic analysis systems can help identify those at risk for serious diseases such as cancer. Automated image analysis tools can examine X-ray and CT scans, looking for disease anomalies. New pathology AI tools can assist in examining biopsy samples, and clinical decision support tools can help providers identify cases of potentially dangerous conditions sooner than conventional diagnosis methods.

Some of the most recent advances include:

• Prediction of Sepsis

The sudden onset of sepsis (blood poisoning due to infection) is a life-threatening condition that must be treated quickly. However, in many cases, it's hard to identify in practice before the condition can advance to a dangerous state. AI-based tools are proving useful in identifying potential sepsis cases earlier, before the need for urgent intervention.

• Pancreatic Cancer Diagnosis

Pancreatic cancer is another disease that typically presents symptoms only after the disease has progressed significantly, often past the point where treatment is effective. New AI-based diagnostic tools can help identify these cancers up to 18 months sooner, offering a lifeline to pancreatic disease patients.

• Pneumonia Diagnosis

A new AI-based algorithm developed at Stanford, called Swarm AI, combines the power of machine learning with direct input from doctors to create a system that is "33 percent more accurate at correctly classifying patients than individual practitioners, and 22 percent more accurate than a Stanford machine-learning program called CheXNet."

Humans are Still Needed to Interpret AI-Sourced Medical Diagnoses to Avoid Dangerous Hallucination Errors

You might have noticed one thing that characterizes these AI-based systems.

Rather than stepping forward and interacting with patients to assess and make diagnoses directly, many of these new-generation AI-based tools work in the background, providing diagnostics information to healthcare providers, who, in turn, must decide if the results make sense.

This is an incredibly important step due to a problem known as AI hallucination.

What is AI hallucination?

You can think of it as an overly eager child (or student or co-worker) who wants to provide an answer even when they don't have the evidence to support their position. Here the AI system is simply filling in gaps in its knowledge to provide a reasonable answer.

Hallucination rates vary, but even if they account for just a few percentage points of the total number of otherwise reliable answers...

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