AI News, Artificial Intelligence (AI) and Mental Health Care

Psychology Today

Dopamine is known as the feel-good neurotransmitter—a chemical that ferries information between neurons.

The brain releases it when we eat food that we crave or while we have sex, contributing to feelings of pleasure and satisfaction as part of the reward system.

This important neurochemical boosts mood, motivation, and attention, and helps regulate movement, learning, and emotional responses.  In lab experiments, dopamine prompts a rat to press a lever for food again and again.

| Time

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How Artificial Intelligence Is Changing Health Care Delivery

These innovations are all powered by artificial intelligence (AI), a burgeoning field of computer science that is already reshaping many aspects of health care by harnessing vast amounts of data to improve diagnosis and treatment, save time and costs, and expand access to care worldwide.

It encompasses natural language processing (where devices like the iPhone’s Siri decipher and respond to human language), machine processing (where technology like self-driving cars process visual data — e.g., identify other cars and pedestrians on the road), and machine learning (where computers learn by experience, such as repeatedly performing and perfecting a skill like playing chess).

However, challenges abound in areas such as data security, patient privacy, legal liability, and the challenges of applying AI tools in new contexts.” Health care AI includes a growing collection of algorithms that drive hardware and software systems to analyze health care data.

AI is expected to continue to improve patient care and meaningfully change the activities undertaken by clinicians, health care provider organizations, payers, pharmaceutical firms, and medical technology companies.

We describe AI’s existing and potential role in reducing workloads, lowering costs, and bettering outcomes across three key domains of health care delivery: administrative work, diagnosis, and treatment.

These tools hold great promise for improving provider efficiency by reducing time spent on manual data entry, although it remains uncertain whether they will live up to their potential, given the steep learning curve for voice-recognition software.

hybrid model…that combines AI-driven smart machines and human experts is likely to be most successful in improving outcomes, as algorithms are more likely to identify false positives (Type 1 error), while clinicians may be more likely to find false negatives (Type II error.” Patient triage represents another promising application of AI in health care delivery.

In another case, an AI-powered therapist has been used to triage mental health patients, and research shows that many veterans with post-traumatic stress disorder (PTSD) feel more comfortable speaking with a virtual chatbot than a human provider.

This type of technology is at an intermediate stage of development (prototype available), but in the future, AI-driven tools could play a significant role in patient triage in both virtual and live settings ranging from the patient’s home to urgent care.

These systems typically use deep learning, a type of machine learning in which human brain-like algorithms learn to solve problems by repeatedly performing tasks (e.g., reviewing different types of tumor scans or pathology slides), without the need for additional human involvement.

In a challenge competition that simulated the reading of pathology slides, seven deep-learning algorithms outperformed a panel of 11 pathologists in detecting lymph node metastases in tissue sections from women with breast cancer.

A hybrid model like this that combines AI-driven smart machines and human experts is likely to be most successful in improving outcomes, as algorithms are more likely to identify false positives (Type 1 error), while clinicians may be more likely to find false negatives (Type II error), as seen in the pathology challenge mentioned above.

Medical AI is likely to emerge in high-resource settings, such as academic medical centers, leading to contextual bias when it is deployed in lower-resource settings such as community health centers or rural areas.” AI can also augment the efficiency of nonvisual diagnostic methods.

Using deep learning, for example, researchers recently described and validated four new phenotypes of sepsis based on clinical data from nearly 64,000 hospitalized patients, expanding clinicians’ understanding of this heterogeneous syndrome.

The key challenges related to incorporating AI into diagnostic processes include integrating algorithms into clinical workflows, demonstrating the safety and effectiveness of algorithms to meet regulatory standards, and consistently updating algorithms with new and more representative data.

One study of 379 patients undergoing minimally invasive spinal fusion surgery found that the robotic-guided technique led to a fivefold reduction in surgical complications at 3 months and 1 year post-operation.

Like the challenges faced around diagnosis, treatment-based algorithms require integrating AI into existing clinical workflows, demonstrating safety and effectiveness above and beyond current best practices, and updating algorithms regularly to reflect representative patient data sets and rapidly evolving medical practices.