As technology pervades every facet of modern life, no other area is as impacted as the healthcare sector. Advancements in medical science have improved life expectancy and the quality of life for patients. However, the downside is the increasing demand for the labor force and the simultaneously rising costs needed to purchase and maintain healthcare systems. AI in healthcare appears to be the light at the end of a tunnel of growing problems.

These mainly originate from an aging population, a massive shift in lifestyle choices (predominantly after COVID-19), and an ever-evolving innovation cycle. According to the United Nations (UN),at least one in every four people across North America and Europe will be elderly (above age 65) by 2050. This naturally translates into complex patient needs.

AI can and already is stepping into the role of assisting healthcare practitioners, making life-saving treatments accessible, and improving patient care experiences. Keep reading as this article delves deeper into the role of Artificial Intelligence in the medical field.

1. Use of Artificial Intelligence in Healthcare Drug Discovery

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So, what is the role of AI in healthcare? It all starts with patient analyses and drug discovery. The National Library of Medicine confirms the direct link between AI and its applications in drug development and discovery.

The first-ever AI in healthcare drug discovery application was announced back in 2020. Since then, the technology is used in the following areas –

  • Target Identification – At the target identification phase, AI is used to understand every disease’s biological mechanisms so that relevant genes and proteins are identified to counteract that disease.
  • Molecular Simulations – Scientists can let go of all traditional chemistry procedures as AI is heavily used in building molecular drug simulations. It is possible to test all candidate drug compounds via these simulations to understand whether a drug is genuinely effective.
  • Drug Predictions – AI is equally used in bypassing simulations by predicting drug properties like toxicity and bioactivity of the molecules.
  • De Novo Drug Design – Traditional drug discovery models involved scanning through different kinds of candidate drug compounds. Today, creating entirely new drug molecules from scratch is possible by using AI in the medical field.
  • Drug Prioritisation – Once a relevant drug has been identified and tested, AI is used to rank these molecules for further testing.

2. AI in Healthcare for Risk Identification

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Healthcare providers can use the predictive analytics capabilities of medical AI in healthcare to recognize patients with high risk. Not only that but also patients displaying similar characteristics, such as medical history and demographics, can be grouped together.

This process, also known as patient stratification, will help in the early intervention for patient preventative care. How does all this happen? Machine Learning in healthcare can scan through huge volumes of Electronic Health Records (EHRs). Patterns and links between patient characteristics and health outcomes may be generated.

For instance – Predictions may say that a particular patient is at high risk of developing cardiovascular disease based on family history, gender, age, etc. In such a case, a cardiologist can create a personalized healthcare plan with dietary recommendations, lifestyle changes, exercise, etc., to lower the risk of heart disease.

3. Machine Learning in Healthcare Triage

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The John Hopkins Medicine Centre researchers made highly effective use of Artificial Intelligence in healthcare. They integrated AI into a patient’s digital health record to help nurses in the Emergency department with better decision-making.

What nurses needed to do was assign each new incoming patient a number, where one stands for ‘sickest’ and five stands for ‘least sick.’ However, since this evaluation is subjective, there were often discrepancies among nurses.

With the integration of AI in healthcare, the nurse simply has to ask for a patient’s details and run their health history through the AI algorithm. The technology then predicts the risk level and recommends suitable triage care. Even the explanation for such a recommendation is given, all within a few seconds.

The patients are again grouped based on their risk factors. This use case of Artificial Intelligence in the medical field is currently in its nascent stages. In any case, it is designed to help clinicians and not replace their role.

4. Radiology

Since radiology has much to do with generating clinical images, radiologists need to spend hours analyzing and sorting through these images to finalize diagnoses. These images can now be analyzed and processed by an AI technology called Computer Vision (CV). The aim is to offer accurate predictions of diseases.

Today, Machine Learning in healthcare has become so advanced that algorithms can detect even the slightest anomalies to generate accurate classifications. Radiologists can develop advanced 3D models, which can be later used for additional analysis.

They can either take a second opinion or be confident of their original diagnosis. As a result, the radiology diagnosis process becomes faster and reduces unnecessary stress on the radiologist.

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Why AI Should be Used in Healthcare

AI in healthcare is not just used to help healthcare practitioners but also patients and society at large. When clinicians’ decision-making and diagnosis improve, so does the patient’s treatment and prognosis. As such, the following is why AI should be used in healthcare.

  • AI’s predictive analytics capabilities help in the early detection and intervention of diseases.
  • Since AI models can easily retain references, patients can receive personalized healthcare treatments.
  • AI has helped speed up the clinical trial time. This makes patient treatment and recovery faster.
  • AI also reduces the time needed to develop new drugs. Not only that, but it greatly enhances drug design and efficacy.
  • With the help of AI, radiologists can develop more accurate medical images and detect signs of cancer and other conditions early on.

Shaping the Future of Healthcare

As AI in healthcare progresses, it will only enhance patient experience and improve the healthcare practitioner’s practice. Software is being developed to monitor the progress of infectious diseases in real-time.

The technology will continue to be used in its broad three categories – patient-oriented care, clinician care, and operational or administrative care. In the future, AI can handle simple and complex tasks, from answering phone calls to reviewing medical records and developing customized patient treatment plans.