Revolutionizing Mental Healthcare with Artificial Intelligence

With the dawn of the COVID-19 pandemic, mental health has become an area of concern, as more than 1 billion humans every year seek help from clinicians and therapists to cure problems such as depression, anxiety, and suicidal thoughts. This inevitable growing pressure has stretched healthcare and therapeutic institutes to choose smarter technologies such as artificial intelligence (AI) and machine learning (ML) to interact with patients and improve their mental health.

According to new studies found in the Journal of the American Medical Association (JAMA), advanced AI and LLM models can enhance mental health therapies on a larger scale by analyzing millions of text conversations from counseling sessions and predicting patients’ problems with clinical outcomes.

Hence, for a more accurate diagnosis, AI in mental wellness has the potential to lead to a positive transformation in the healthcare sector.

Today’s exclusive AI Tech Park article explores the transformative potential of AI in mental healthcare.

Decoding Mental Health Therapies With AI

In contrast to physical health specialties such as radiology, cardiology, or oncology, the use of AI in mental healthcare has been comparatively modest; where the diagnosis of chronic conditions can be measured by laboratory tests, mental illness requires a complex and higher degree of pathophysiology, which includes a major understanding of genetic, epigenetic, and environmental and social determinants of a patient’s health. To gain more accurate data, mental healthcare professionals need to build a strong and emotional rapport with the patient while being observant of the patient’s behavior and emotions. However, mental health clinical data is quite subjective, as data comes in the form of patient statements and clinician notes, which affect the quality of the data and directly influence AI and ML model training.

Despite these limitations, AI technologies have the potential to refine the field of mental healthcare with their powerful pattern recognition technologies, streamlining clinical workflow, and improving diagnostic accuracy by providing AI-driven clinical decision-making.

The Dilemma of Ethical Considerations

As the world moves towards digitization, it is quite noteworthy that the mental healthcare sector is gradually adopting AI and ML technologies by understanding the technicalities, adhering to rules and regulations, and comprehending the safety and trustworthiness of AI.

However, it is often witnessed that these technologies come with drawbacks of varying accuracy in finding the correct psychiatric applications; such uncertainty triggers dilemmas in choosing the right technology as it can hamper patients’ health and mental well-being.

In this section, we will highlight a few points where mental healthcare professionals, AI professionals, and data engineers could collaborate to eliminate ethical issues and develop trustworthy and safe AI and ML models for patients.

Overall, the promising development of AI in healthcare has unlocked numerous channels, from cobots helping surgeons perform intricate surgeries to aiding pharmaceutical companies and pharmaceutical scientists to develop and discover new drugs without any challenges.

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Cobots Enhance Efficiency and Care in Healthcare

The term robot was invented by the Robotics Institute of America in the late 1940s as a machine that executes mechanical functions just like human beings but lacks the same “sensitivity” as a human.

Initially, robots were designed and employed to work like humans, especially in the manufacturing industry, FMCG industry, and automotive industry, but later, robotics engineers observed that they were capable of working in other application areas due to their ability to multitask efficiently and faster. Hence, robotics engineers introduced medical robotics into the healthcare sector, causing a drastic change in the outlook for treatment and diagnostics.

With advances in technologies, the utilization of robotic devices has upscaled from spearheading lab tests to automating medical procedures to collaborative robotics (cobots), helping healthcare workers in surgeries and improving treatment outcomes with negligible error encounters.

This exclusive AI Tech Park article focuses on the application of cobots in healthcare while keeping ethical considerations in mind. In addition, we will also focus on the ongoing research and development of robotics in healthcare.

Application of Collaborative Robots in Healthcare

In this section, we will highlight the application areas where collaboration between robots and healthcare practitioners can improve healthcare outcomes, such as helping with surgeries or offering personalized patient care. The below subtopics explore the different aspects of collaborative robots with humans.

Cobots in Rehabilitation and Physical Therapy

The rehab cobots are developed to help impaired and injured patients recover from accidents so that they can lead normal lives. One will find a variety of rehab robots that are intended to help patients with numerous medical conditions, including cerebral palsy, stroke, and injuries to bones or muscles. One such example is Orthoses, a robotic exoskeleton system that assists paralyzed patients in limb movement. This system works on “pre-set user-defined commands” that are fed into the robot to read the user’s mind and act accordingly.

Some studies show that children with autism spectrum disorder (ASD) have a positive reaction to therapies when interacting with cobots, as they have various AI functions such as playing games and recognizing facial expressions that keep patients motivated and entertained throughout the therapy journey. For instance, the Keepon robot developed by Hideki Kozima is a unique robot that studies autistic behavioral changes in children and monitors their overall health.  

Cobots in Precision Surgery

With the advancement of technology, cobots have been assisting surgeons in critical operations. These surgery cobots are categorized under Active Surgical Systems, which pre-program electronics and can work autonomously; Master-Slave Systems, which are totally under the control of surgeons; and Semi-Active Systems, which allow surgeon-driven electronics with pre-programmed electronics. The most commonly used cobot system (Master-Slave System) in the healthcare industry is the DaVinci system, a robotic arm that mimics the surgeon’s hand movement into smaller and more precise actions for less invasive and complex surgeon procedures.  

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