AI Articles and Analysis in Healthcare

According to data from the U.S. Department of Health and Human Services, the progress of the value-based healthcare delivery system in the U.S. — a provider payment model based on patient outcomes — has run almost parallel to the significant implementation rate of electronic health records/electronic medical records (EHR/EMR).

Episode summary: In this episode of "AI in Industry" podcast, we speak with Manoj Saxena, the Executive Chairman of CognitiveScale, about how AI and automation are being applied to white-collar processes in the healthcare sector.

The healthcare industry is evolving into an increasingly digital environment, and as a result cybersecurity continues to be a top priority for protecting sensitive data such as financial records and patient medical records.

AI and machine learning software are beginning to integrate themselves as tools for efficiency and accuracy within pathology. According to a March 2018 digital pathology report produced by Allied Market Research, such software enables the “procurement, management and interpretation” of information and can be applied to multiple functions within the field of healthcare.

The opioid overdose crisis continues to maintain a strong grip on the nation. According to the National Institute on Drug Abuse (NIDA), the epidemic claims more than 115 lives in the United States every day.

According to the World Health Organization (WHO), the array of mental disorders include depression, bipolar affective disorder, schizophrenia and other psychoses, dementia, intellectual disabilities and developmental disorders including autism.

Hearing loss is a condition affecting an estimated 48 million individuals in the U.S. While the degree of severity may vary across age groups, the Hearing Loss Association of America reports that hearing loss ranks third after arthritis and heart disease among the most common physical ailments.

Artificial intelligence -- especially machine learning -- is at it’s best when it is working with a large, analyzable data set, like text. But much of the data in the world isn’t in text form. Instead, it’s in the form of spoken words on video and audio recordings or even live events. This makes reliable voice transcription an important goal for artificial intelligence.