Advent of AI in healthcare

Although Artificial intelligence (AI) is touching every sphere of our lives, especially in the healthcare domain, its impact is truly life changing. Secondary research reveals, jointly public and private sector investment in healthcare is expected to reach $ 6.6 billion by 2021, encompassing areas such as healthcare, clinical research, drug development and insurance. Although human physicians will not be replaced by AI doctors however, AI will definitely prove useful in simplifying clinical decision especially in the area of radiology. Through imitation of human cognitive functions, it enhances the sector’s availability of both, structured such as image, genetic and electrophysiological data and unstructured data such as clinical notes and medical journals and also analytical techniques through machine learning and natural language processing (NLP), respectively.

Acceptability of AI

In healthcare, AI is effective in areas of early detection, diagnosis, treatment, prediction and prognosis evaluation in diseases such as cancer, neurology and cardiology. Through application of big data analytics, one can gather relevant information from piles of healthcare data, such insight can assist in clinical decisions. Also, AI helps incorporate self-correcting abilities and facilitates auto updation of medical information in different healthcare devices. Its ability to reduce diagnostic and therapeutic errors, create real-time health risk alerts and predict health outcomes are gaining traction over time. NLP is particularly helping in translating text to machine-readable structured data, which can thereafter be analyzed by ML techniques.

Prevalence of AI devices in healthcare

It is a complex task to accurately diagnose diseases, wherein AI-based tools with its enhanced functionalities make the entire process seamless, ensuring precision. It improves patient’s interaction with healthcare providers, systems and services. The different AI devices that finds broad usage in medical applications are:

1.     The classical machine learning techniques

2.     Deep learning techniques

3.     NLP methods

Prerequisite of AI in healthcare

Healthcare applications need training on data generated from clinical activities, such as screening, diagnosis and treatment before installing AI systems. Clinical data includes data obtained from demographics, medical notes, electronic recordings from medical devices, physical examinations of patients and laboratory images. Such data holds value in diagnosis of disease or in detecting genetic abnormalities. This is particularly effective in diagnosis of gastric cancer or neural injury.

Impact of AI technologies in diagnosis

  • IBM Watson is a reliable AI system to diagnose cancer through a double-blinded validation study.
  • Google’s DeepMind Health can combine machine learning with neuro science to build an algorithm with neural network that can detect medical conditions faster.
  • AI in healthcare can help restore the control of movement in patients with quadriplegia through spinal motor neurons to regulate upper-limb prostheses.
  • AI can help diagnose heart disease through cardiac image and create a remedy through automated editable ventricle segmentations.
  • For chronically ill patients, disease management and care plans can be approached in a comprehensive manner.

Prominence of AI in healthcare

AI and robotics are encompassing all aspects of our healthcare ecosystem. It is efficient, quick and cost effective at the same time. AI and Internet of Medical Things (IoMT) has already created a huge impact by encouraging individuals with a healthy lifestyle and proactive health management. The following are the importance of AI in healthcare.

  1. In southeast England, patients were given AI-powered, Wi-Fi-enabled armband that can monitor respiratory rate, oxygen levels, pulse, blood pressure and body temperature of patients. Also, readmission of patients costed US hospitals $40 billion annually so, Grady Hospital, the largest public hospital in Atlanta reduced readmission rates by 31% over a period of two years by adopting AI tools.
  2. Research suggests that with deployment of AI in healthcare, home visits of medical practitioners reduced by 22% and long-term completion of treatment by patients increased by 96%. AI driven systems constantly monitor and analyze warning signs alerting both, patients and professionals before healthcare is needed.
  3. According to World Health Organization, in 60% cases, the health of an individual and their lifestyle are correlated so, AI based systems can now trigger reminder and also generate alert based on vital signs of patients and remind them to take prescribed medicine.
  4. Risk can be mitigated effectively through alliance. Synaptic Healthcare Alliance, a collaborative pilot program between Aetna, Ascension, Humana and Optum use blockchain method to manage data and utilize AI efficiently. With the tone of voice and background noise, AI in healthcare can detect cardiac arrest with 93% success rate. According to UK’s healthcare system, AI can prevent thousands of cancer related deaths by 2033.
  5. AI collaborates with trained professionals to diagnose competently. For example, University of California at San Diego depends on AI to successfully diagnose childhood diseases.
  6. AI reduces the need for biopsy as it has the ability to translate mammograms 30 times faster with 99% accuracy.
  7. AI plays an important role in drug research and discovery.
  8. AI technology used in surgery can reduce 21% of patient’s hospital stay due to minimal incision. Heart lander, a miniature robot is used in heart surgery with precision and competency.
  9. A virtual nursing assistant can save $ 20 billion annually and can interact with patients creating an effective care setting.
  • AI can also automate administrative task saving $ 18 billion for healthcare industry. For example, IBM’s Watson helped Cleveland Clinic’s physicians by analyzing thousands of medical papers using NLP to create treatment plans.
  • AI can improve next generation radiology tools that will no more rely on tissue samples. It will be able to analyze 3D scans 1000 times faster than human minds.

Conclusion

In spite of the potential benefit of AI in healthcare, there are certain challenges in adopting and implementing it. Till date, both healthcare professionals and patients haven’t been able to completely rely on algorithms to diagnose and plan treatment. However, slowly but steadily individuals are adopting AI tools that can accurately diagnose issues, analyze clinical reports and identify genetic information in a much faster pace that can save lives. It is worth mentioning here how GAVS Technologies empowered multiple hospitals and healthcare organizations around the world, with technology-led SMART solutions and delivery, that has significantly improved patient care. Visit www.zif.ai to know more.