Machine Learning System Catches Two-Thirds More Prescription Medication Errors than Existing Clinical Decision Support Systems at Two Major Hospitals

Researchers find a savings of more than one million dollars and prevention of hundreds, if not thousands, of adverse drug events could have been had with machine learning system Support for artificial intelligence (AI) and machine learning (ML) in healthcare has been mixed among anatomic pathologists and clinical laboratory leaders. Nevertheless, there’s increasing evidence that diagnostic systems based on AI and ML can be as accurate or more accurate at detecting disease than systems without...

Could Biases in Artificial Intelligence Databases Present Health Risks to Patients and Financial Risks to Healthcare Providers, including Medical Laboratories?

Clinical laboratories working with AI should be aware of ethical challenges being pointed out by industry experts and legal authorities Experts are voicing concerns that using artificial intelligence (AI) in healthcare could present ethical challenges that need to be addressed. They say databases and algorithms may introduce bias into the diagnostic process, and that AI may not perform as intended, posing a potential for patient harm. If true, the issues raised by these experts would have...

New FDA Regulations of Clinical Decision-Support/Digital Health Applications and Medical Software Has Consequences for Medical Laboratories

Softened FDA regulation of both clinical-decision-support and patient-decision-support software applications could present opportunities for clinical laboratory developers of such tools Late 2017, the Food and Drug Administration (FDA) released guidelines on how the agency intends to regulate—or not regulate—digital health, clinical-decision-support (CDS), and patient-decision-support (PDS) software applications. The increased/decreased oversight of the development of these physicians’ tools...
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