The Germination of Artificial Intelligence in Healthcare

Artificial Intelligence (AI) has quickly evolved to play a strong role in the world over the past few decades. What most people don’t realize is that AI presents itself in many forms which affect daily life. Logging into your social media, email, car ride services, and online shopping platforms all involve artificial intelligence algorithms to expand the user experience.

One major area where AI is growing rapidly is the healthcare field—specifically in diagnostics and treatment management.

Machine Learning: A Subset of AI in Healthcare

Machine learning is one of the most common forms of AI in healthcare. It is a broad technique at the core of many approaches to AI and healthcare technology and there are many versions of it.

Machine learning offers data-driven clinical decision support (CDS) to physicians and hospital staff, paving the way for improved care and reduced cost. Deep learning, a further subset of machine learning, is designed to identify patterns and uses algorithms and data to give automated insights to healthcare providers.

Artificial Intelligence in Healthcare

The AI systems in healthcare are always working in real time, which means the data is always updating, thus increasing accuracy and relevance. Assembled data is a compilation of many medical notes, electronic recordings from medical devices, laboratory images, physical examinations, and demographics. With this compilation of endlessly updating information, practitioners have almost infinite resources to expand their treatment capabilities.

AI is one of the biggest advancements in healthcare. For some, AI conjures images of a wicked super intelligence that will outperform humanity across the board. For others, AI signifies a confluence of machine learning algorithms and data that allows for more calculated decision-making. Much like smartphones and apps, AI is healthcare’s next leap forward.

Until we have adequate annotated training data and human-like intelligence that can transfer knowledge and learnings from one domain to another, AI in healthcare is very dependent on supervision by human doctors.

A collaboration between AI startups and healthcare systems are giving deep learning networks tens of thousands of human-labeled examples and the future of AI’s relationship with healthcare looks bright. In fact, AI-Integrated Operating Rooms are pioneering a safer, more efficient surgical process.

AI in the Healthcare Market

  • Patient Data and Risk Analysis- Patient risk algorithms consider numerous variables and express the results as the proportional risk of developing a major fatal or nonfatal disease in the coming years. Companies functioning in this sector cater to the needs of healthcare experts, including healthcare providers and payers, by offering them solutions that can provide predictive insights into patient health using machine learning and natural language processing algorithms. The analytics are based on various factors, including medical history and demography.
  • Inpatient Care & Hospital Management- AI and machine-learning methods have the potential to develop the quality and lower the cost of patient care. Clinical decision support systems (CDSS) are the most successful applications of AI in inpatient care and hospital management. Recent advances in machine learning and AI can help build predictive models and make real-time inferences from a large patient population for analyzing risks and predicting the length of a hospital stay. Such developments are driving the progress of AI in the healthcare market for this application segment.
  • Medical Imaging & Diagnostics- In healthcare, medical imaging produces a large volume of data, and this data is also the most challenging of all in terms of understanding and analysis. Healthcare AI startups are raising venture capital and have been working in the field of imaging and diagnostics (especially pathology) to extract images using machine learning.

  • Drug discovery- AI algorithms ingest and analyze a vast amount of info and can identify potential drug candidates in a short time. AI also plays a significant role in drug discovery for chronic diseases like cancer. AI significantly reduces the time taken to bring a cancer-combatting drug to the market. Recently, AI has also been used to develop a drug against COVID-19. According to the National Science Foundation (US), researchers across the world are racing toward the development of a vaccine or drug, or a combination of both to treat COVID-19. The researchers are leveraging AI along with physics-based drug docking and molecular dynamics to classify molecules that might interact with the virus.

  • Virtual Assistant- The use of AI-based personal assistants can have an unbelievable effect on monitoring and assisting patients in the absence of clinical personnel. A virtual assistant can offer discharged patients more flexibility by capturing data through their voices and responding accordingly. By using NLP, virtual assistants can better understand patient needs and conditions and assist with inpatient care.

AI and Top Technologies

  • Machine Learning- Machine learning is helping healthcare find intelligent ways to automate data analysis. It is used to update administrative processes in hospitals, map and treat infectious diseases, and personalize medical treatments. Imaging and diagnostics, and drug discovery are among the applications that use deep learning.

  • Computer Vision- In healthcare, computer vision has proven to be useful in the  surgery and the therapy of a few diseases. Robotic surgery application uses computer vision to recognize distances or specific body parts. Computer vision systems offer accurate diagnoses, thus minimizing false positives. The technology can potentially wipe out the requirement for redundant surgical procedures and expensive therapies.

  • Natural Language Processing- NLP is widely used by the clinical and research community in healthcare to develop and manage semi-structured and unstructured textual documents, such as electronic health reports, pathology reports, and clinical notes. The demand for NLP has grown, with healthcare institutions using it to structure their clinical data and interpret it more accurately.

Moreover, the growing use of the internet and connected devices, along with the huge volume of patient data, drives the growth of this market.

Looking Ahead

While this technology still has a few valid reasons for caution, artificial Intelligence tools can aid the medical industry in allowing faster service, more accurate diagnosis, and data analytics for identifying trends or any genetic information that would expose someone to a specific disease. 

We exist in an era where saving even a couple of minutes can save lives and in these times, artificial intelligence and machine learning can be transformative, not only for healthcare but for every single patient.

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