7 Recent Applications of AI In Healthcare

How Artificial Intelligence is complementing healthcare practitioners


Wow AI

2 years ago | 8 min read

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Artificial Intelligence (AI) has made a major impact across a myriad of industries, especially in healthcare. This trending technology that was only a dream once is not so anymore. Rather, this evolving technology has become a part of our daily lives in ways we never have imagined. The use of AI in the healthcare industry is radically changing the face of the IT industry. This blog on the applications of AI in healthcare discusses various uses of Artificial Intelligence in this industry. So, let’s learn about this technology and its uses in detail.

Professionals trouble themselves by thinking that AI and Machine Learning systems will take over their jobs in the coming years. Although this technology is substituting humans in a plethora of job roles across industries such as marketing, finance, telecommunication, and more, it is not reducing the job opportunities. AI is leading to the creation of a range of new job vacancies that did not even exist a few years back. In the field of healthcare, it plays a vital role that you will read about further.

Role of AI in Healthcare

Artificial intelligence (AI) is reshaping healthcare, and its use is becoming a reality in many medical fields and specialties. AI, machine learning (ML), natural language processing (NLP), and deep learning (DL) enable healthcare stakeholders and medical professionals to identify healthcare needs and solutions faster with more accuracy, using data patterns to make informed medical or business decisions quickly.

How AI works in healthcare

AI can analyze large amounts of data stored by healthcare organizations in the form of images, clinical research trials, and medical claims, and can identify patterns and insights often undetectable by manual human skill sets.

AI algorithms are “taught” to identify and label data patterns, while NLP allows these algorithms to isolate relevant data. With DL, the data is analyzed and interpreted with the help of extended knowledge by computers. The impact of these tools is huge, considering a Frost & Sullivan analysis indicated artificial intelligence and cognitive computing systems in healthcare will account for $6.7 billion this year from the market compared to $811 million in 2015.

The use of AI is supporting many stakeholders in healthcare:

  • Teams of clinicians, researchers or data managers involved in clinical trials can speed up the process of medical coding search and confirmation, crucial in conducting and concluding clinical studies.
  • Healthcare payers can personalize their health plans by connecting a virtual agent via conversational AI with members interested in customized health solutions.
  • Clinicians can improve and customize care to patients by combing through medical data to predict or diagnose disease faster.

Top 10 AI use cases in Healthcare

Healthcare “Data Mining” with AI can predict diseases

In the era of ubiquitous technology, data becomes an important fuel to drive innovation. Data mining is being deployed to find insights and patterns from large databases. The healthcare industry captures large volumes of patient records. With appropriate analysis of this data, using machine learning tools, the healthcare sector can address a plethora of diseases before their occurrence.

Currently, the healthcare industry employs data mining to develop early detection systems by using clinical and diagnosis data. Tech giants, such as Google and IBM are using AI to unearth patient data which are structured and unstructured. The data is extracted by mining the medical records or by deciphering physician-patient interactions (voice and non-voice-based interactions).

AI in “Medical Imaging and Diagnostics” provides precise information

Over the past couple of years, as a top example of AI use cases in healthcare, it is witnessed that AI has expanded substantially in the fields of medical imaging and diagnostics, thereby enabling medical researchers and doctors to deliver flawless clinical practice. Paving the way for quantification and standardization, deep learning is aiding in the prevention of errors in diagnostics and improving the test outcome.

Further, AI is improving the assessment in medical imaging to detect cases such as malignancy and Diabetic Retinopathy (DR). It is also assisting with quantifying blood flow and providing visualization. According to European Radiology Experimental’s recent poll, over 50% of global healthcare leaders expect the role of AI in monitoring and diagnosis to grow significantly. Recently, Arterys, a Deep Learning medical imaging technology company, partnered with General Electric (GE) Healthcare. This partnership combines Arterys’ quantification and medical imaging technology with GE Healthcare’s Magnetic Resonance (MR) cardiac solutions. By collaborating these technologies, it is now possible to conduct cardiac assessments in a fraction of the time as compared to the conventional cardiac MR scans.

AI in “Lifestyle Management and Monitoring” is changing the way we live

A very important aspect of AI use cases in the healthcare sector is the increase in digitization that enables individuals to manage their health and comfort. Data generated from digitization fuels the AI technology of tomorrow. Today, parents can monitor their infants to check their health, sleeping patterns, and development. Recently, Fedo, a start-up, found a solution to encounter individual risks for lifestyle diseases. They have developed a risk stratification algorithm, using AI, to predict individuals’ readiness for 7 non-communicable diseases such as Diabetes II and Cardiovascular disease — Myocardial Infarction.

AI in “Nutrition” is enhancing the journey to a healthy and fit lifestyle

Currently, an extensive number of nutrition-related apps are available in stores, with different functions and accuracies. With the integration of AI, nutrition apps can become a very good example of AI use cases in the healthcare sector, which can give customized recommendations and suggestions based on a person’s preferences and habits. VITL, a start-up based in London, is applying AI to diagnose patients’ nutritional needs and deficiencies. Along with the diagnosis, it further provides users with a bespoke nutrition plan and daily vitamin pack. To map out the logic and thought process of human nutrition experts, the start-up uses an AI engine called LANA (Live and Adaptive Nutritional Advisor) which employs a broad range of lifestyle and diet data points.

AI in “Emergency Room and Surgery” is saving lives

The first surgical robot, named da Vinci Surgery System, which was approved by the FDA for general laparoscopic surgery, was developed about 15 years ago. Since then, many other surgical robots were introduced, including the current generation of robots which are integrating AI into surgery. The next generation of surgical robots is being powered by machine learning and AI. Shortly, we will witness AI platforms such as DeepMind, IBM Watson, and other advanced AI tools enabling physicians and hospitals to deliver promising surgical interventions. IBM Watson has advanced medical cognitive and NLP capabilities to respond to surgeons’ queries. Further, similar AI platforms aid in monitoring blood in real-time, detect physiological responses to pain and can provide navigation support in arthroscopy and open surgery.

AI in “Hospital Information System (HIS)” can enrich the delivery of healthcare services

Currently, most hospitals and clinics have HIS software to handle the process of appointments, treatment follow-up, and other administrative processes, by integrating with the EHRs of patients. There is great potential for these systems to be used for offering superior health services. For instance, Google’s DeepMind Health team is working with NHS hospitals to monitor a patient’s conditions via a mobile application. The app allows the hospitals to promptly identify any deterioration in the patient’s conditions and thus provide treatment as quickly and accurately as possible. Furthermore, AI in healthcare provides support to clinicians for predictive analytics in real-time and solves operational challenges across the hospital functions. It also saves staff time, reduces steps, and removes paper-based processes through automated data collection, analysis, reporting, and communication.

AI in “Research” is providing fascinating insights

AI enables healthcare providers to create a digital profile of humans. This can help in understanding immunosequence, thereby generating a new class of immune diagnostics in oncology. Additionally, it is being used to accomplish reproducible research in bioinformatics, genomics, and life science. Adaptive Biotechnologies, a start-up addressing genomic-based therapy, partnered with Microsoft to find out insights into immnosequence.

AI in “Mental Health” is building a strong support system for patients

While talking about AI use cases in healthcare, let’s not miss out on AI in “mental health”. We live in a world where 1 in 4 people suffer from mental disorders, making it one of the leading causes of disability and ill-health. Healthcare, being relatively slow in the adoption of new technologies, has seen some of the greatest advances in AI recently including early identification of mental health symptoms. Certain factors such as a person’s tone, word choice, and the duration of a phrase are considered when studying an individual..

Wysa, an AI-based emotionally intelligent penguin, developed by Touchkin, can listen, chat and help users build mental resilience. Within 3 months, Wysa had witnessed a million chats with 50,000 users and assisted them to overcome mental health troubles. Some of these users had been suicidal, and others lived with Post-traumatic stress disorder (PTSD), social anxiety, depression, or bipolar disorders. Thus, for the millions of people who feel lonely and need the support of friends and psychiatric therapists, AI can build resilience, offer support, and save lives.

AI in “Pharma” is enabling the discovery of a new class of diagnostics and treatment

AI is revolutionizing the way pharmaceutical companies develop medicines. AI searches biological systems to understand how a drug can affect a patient’s tissues/cells. For instance, applications like precision medicine and predictive medicine are used to predict a patient’s treatment rather than investigating a bigger set of patients. BERG, a pharmaceutical start-up, has created an AI platform that uses biological data as cells transform from healthy to malignant ones. The software utilizes information from the 2003 Human Genome Project in addition to over 14 trillion data points in single cell tissue. This research allowed BERG to develop a new cancer drug that could potentially reverse this process.

AI technology in “Virtual Assistant” to communicate with patients in an efficient way

AI use cases in healthcare can also be discussed primarily with the introduction of Virtual Assistants/AI assistants being created to help and enhance human-like interactions, thereby saving time and resources. Nuance, a company that has developed a Medical Virtual Assistant, streamlines clinical workflows for the 500,000 clinicians who rely on Dragon Medical every day for their clinical documentation. It enables individuals who are using specialized medical terminologies to communicate naturally with high accuracy.

Building scalable, practical, and responsible AI models in healthcare

Responsible AI is concerned with the design, implementation, and use of ethical, transparent, and accountable AI technology in order to reduce biases, promote fairness, and equality, and help facilitate interpretability and explainability of outcomes, which are particularly pertinent in the healthcare context. However, the extant literature on health AI reveals significant issues regarding each of the areas of responsible AI, posing moral and ethical consequences. This is particularly concerning in a health context where lives are at stake and where there are significant sensitivities that are not as pertinent in other domains outside of health.

During the development phase of building AI models in healthcare, many companies are struggling with data sourcing and model testing. How to build scalable, practical, and responsible AI models in healthcare is also one of the topics featured in our Worldwide AI Webinar with speakers from tech giants such as Google, IBM, SAP, AWS, etc.

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