Artificial Intelligence, A gift for Healthcare

For decades, Artificial Intelligence (AI) has proven indispensable to many different businesses. It is only recently that AI has emerged as a key player in the medical field. More than $1 billion USD has been raised in start-up equity, and a recent McKinsey analysis predicted healthcare would be one of the top five industries to deploy AI in more than 50 use cases. If you are looking for the cheapest place to study abroad for Indian students, connect with Roy Overseas - one of the top overseas education consultants in India that can fulfil your dream of studying abroad.

What is AI?

The notion of AI dates back to the 1950s, when it was envisioned as a means to programme a computer to learn and reason in human-like ways. Companies like Facebook and Google use AI extensively. However, artificial intelligence has only made modest advances toward a big and multifaceted promise in the health care sector.

Awareness of AI in health care industry:

The health care field is only one area where AI is showing promise as a game-changer. Here are a few current examples:

-      Radiology:

In order to automate picture processing and diagnosis, scientists are working on AI solutions. This can help radiologists save time and avoid making mistakes by drawing their attention to relevant parts of a scan. Fully automated methods exist to read and interpret a scan automatically without human oversight, which could allow offer quick interpretation in under-served areas or outside of business hours. Newer opportunities to prevent cancer are illustrated by recent demonstrations of enhanced tumor identification on MRIs and CTs.

-      Drug Discovery:

To combat imminent dangers like the Ebola virus, scientists are working on AI solutions to sift through the information databases on existing drugs in search of new possible remedies. This has the potential to increase the effectiveness and speed with which novel medications are brought forward in response to life-threatening illness risks.

-      Patient Risk Identification:

AI systems can aid clinicians in real time by analyzing massive volumes of patient data from the past to assisting in identifying at-risk patients. Re-admission concerns are being highlighted, with particular attention being paid to those patients who have a high probability of readmission within 30 days after discharge. The growing resistance from payers to cover the expenses of hospitalization associated with readmission is motivating multiple health systems as well as companies to create solutions based on data in the patient's electronic health record. Other recent research has shown that a single retinal scan can be used to assess a patient's risk of developing cardiovascular disease.

-      Primary Care:

Multiple groups are developing voice or chat-based direct-to-patient systems for triage and advice. Because of this, people can get answers to their most fundamental medical queries quickly and on a large scale. Potentially lowering the burden on overburdened primary care doctors, this might also give those living in under-served areas access to basic health advice for a select group of illnesses. While the idea is sound, extensive independent validation is still required to demonstrate the treatments' safety and efficacy for patients.

The health care industry makes use of numerous AI variants. Algorithms for NLP have given computers the ability to comprehend and respond to human speech. Algorithms designed for ML can help computers learn to draw conclusions and draw inferences from otherwise incomprehensible data sets.

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There are many ways in which AI is currently impacting the health care industry, and its future potential is staggering. We have identified four key areas in which AI is revolutionizing the health care sector below.

4 Ways AI Is Transforming Health care:

This game-changing innovation has the potential to enhance diagnoses, expand treatment options, encourage patient compliance and participation, and facilitate more effective management and operations.

1.  Improving Diagnostics

Artificial intelligence (AI) can assist doctors in making diagnoses by assessing symptoms, making therapy recommendations, and making risk assessments. Anomaly detection is another useful feature.

Intelligent symptom checkers are already being used by many health care practitioners and institutions. Patients are guided through a line of questions regarding their symptoms by this machine learning system, which then recommends the next steps for them to take in terms of getting medical help. Buoy Health provides a web-based AI health assistant that hospitals and clinics are utilizing to screen patients with COVID-19 symptoms. It provides individualized analysis and suggestions in accordance with current best practices from the CDC (CDC).

And because of AI's ability to analyze large amounts of data and generate conclusions, precision medicine, or individualized health care, can be taken to a whole new level. In order to help clinicians choose the best treatment for their patients, deep learning models may sift through vast volumes of data, such as the patient's genetic makeup, other molecular/cellular studies, and lifestyle factors.

To further aid in patient care, health care AI can be utilized to create algorithms that forecast health risks for both individuals and entire populations. In order to predict the start of sepsis or septic shock in patients 12 hours in advance, clinicians at the University of Pennsylvania employed an ML algorithm that helps in monitoring numerous important indicators in real time.

lDetecting Disease:

The process of diagnostics for doctors can be aided by imaging equipment. Enlitic, based in San Francisco, creates deep learning medical solutions to enhance radiological diagnostics via data analysis. In this way, clinicians can gain a deeper insight into and more precisely define tumors' aggressiveness. These resources can help clinicians determine the phenotypic and genetic features of tumors without requiring physical tissue samples.

It has also been shown that these imaging techniques can draw more accurate judgements than doctors can. 7 out of 32 deep learning algorithms were able to detect lymph node metastases in breast cancer patients with higher accuracy than 11 pathologists together, according to a study published in JAMA in 2017.

In addition to the potential benefits in the fields of dermatology and ophthalmology, smartphones and other portable gadgets may develop into potent diagnostic tools. Differentiating between malignant as well as benign skin diseases and evaluating and classifying photographs are two primary uses of medical AI in dermatology. Telehealth's reach could be expanded with the help of smartphones' image-gathering and sharing features. Remidio, a medical technology firm, has developed a fundus camera—basically a low-powered microscope with a connected camera—that can be used on a smartphone to identify diabetic retinopathy.

2.  Advancing Treatment:

Artificial intelligence in medicine is rapidly maturing into a useful clinical tool. Those patients having lost their capacity to talk or move may be able to regain it with the use of brain-computer interfaces. Patients with ALS, stroke, or spinal cord injuries may also benefit from this device.

Currently, only 20% of patients react to immunotherapy; nevertheless, there is promise for ML algorithms to increase its utilization. The advent of cutting-edge scientific tools has opened up the possibility of tailoring therapeutic interventions to each person's own genetic profile. AI and ML are being used by various companies to create innovative treatments.

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Better medical decisions can be made by health care professionals with the help of CDSSs, which analyze historical, present, and future patient data. IBM provides clinical assistance technologies that can assist a doctor in making a better educated and evidence-based diagnosis and treatment plan for a patient.

Finally, AI may speed up medication research by cutting down on the price and length of drug discovery. Artificial intelligence systems facilitate data-driven decision making, instructing scientists on which substances warrant additional investigation.

3.  Enhancing Adherence and Engagement of Patients:

Activity trackers as well as smart watches are only two examples of the wearable and individualized medical equipment that can aid in the monitoring of health for both patients and doctors.

In addition to aiding in treatment adherence, these gadgets can be quite helpful. Patient compliance with prescribed treatments may influence final results. Care plans can backfire if patients aren't willing to make the necessary behavioural changes or adhere to the suggested medication schedule. Artificial intelligence's potential to personalize care has the potential to keep patients actively participating in their treatment plans. Using AI, we may notify patients of important information or provide them with content that will hopefully motivate them to take some sort of action. Companies like Livongo are aiming to provide consumers with customized "health nudges" in the form of alerts that highlight options that are beneficial to their emotional and physical well-being.

With the help of AI, we can build a self-service model for patients that is more flexible and user-friendly than ever before: a web portal that can be accessed from any mobile device. Providers can save money and patients can get all the necessary care more quickly with the support of a self-service approach.

4.  Facilitating Business Processes in Administration and Operations:

By automating some of the administrative and operational tasks, AI can help the health care system run more efficiently. One of the biggest reasons of lost productivity among physicians is the time spent recording notes and examining the electronic health records, which accounts for 34–55 percent of a doctor's workday. NLP clinical documentation systems can save up doctors' time so they can focus on patient care rather than paperwork.

AI can also help the health insurance industry. Since 80% of their claims are recognized as wrong or fraudulent by insurers, the existing method of analyzing claims is highly time-consuming. Insurers may use NLP techniques to spot problems in seconds instead of days or months.