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AI Future in Healthcare: Improving the Effectiveness of Medical Care

Vahan Zakaryan
2 Dec 2021
7 min
AI Future in Healthcare: Improving the Effectiveness of Medical Care

In 2016, Azad Shademan and his colleagues supervised a soft tissue surgery (intestinal anastomosis) in living pigs, performed by the Smart Tissue Autonomous Robot (STAR). Before you start calling PETA and complaining about the inhumane treatment of an animal, here are a few interesting facts you should know about this event.

First, it was a 60%-autonomous surgery, where suturing strategy and visualization were based entirely on artificial intelligence (AI) algorithms. Second, compared to three other methods used to validate the hypothesis – hand suturing, laparoscopy, and the robot-assisted da Vinci Surgical System – STAR surgery “quantitatively outperformed human surgeons in a series of ex vivo and in vivo surgical tasks.” Finally, the pig lived after the surgery. 

We’ve brought up the story of the first-ever robot-operated surgery to demonstrate that AI in the healthcare sector is not just a trend — it’s a technology that will drive better, safer, and more precise medical services. However, the scope of its use goes beyond the operating room. This article will review the current state of AI in healthcare, its market share, limitations, challenges, and future outlook. 

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    AI in healthcare: Market overview 

    Presented as software solutions, hardware, and services, healthcare AI is predicted to grow annually. In 2021, its estimated worth is $10.4 billion, whereas, in 2028, it’s expected to reach the $120.2 billion mark. This 41.8% compound growth rate is associated with the following factors: 

    • The inflow of patient-related data 
    • Demand for personalized medicine
    • Fast population aging

     The USA and China are the leading countries with the highest VC funding in medical AI.

    AI Future in Healthcare: Improving the Effectiveness of Medical Care - photo 1

    The potential of AI in healthcare is tremendous, which makes it one of the most attractive niches for investments. Let’s review where it’s already applied.

    How is AI used in healthcare today?

    In broader terms, AI in healthcare refers to applying machine learning (ML) and cognitive technologies in medical settings and for medical purposes. There are plenty of practical AI use cases at various levels of healthcare — from medical research to identifying at-risk patients and training the machines to interpret a lot of data in seconds. With AI-powered tools and software, people can keep track of their vital indicators, have their medical data interpreted, and get alerts about potential risks. The diagram below shows how AI can be used in healthcare currently.

    AI Future in Healthcare: Improving the Effectiveness of Medical Care - photo 2

    While some AI solutions are at the development and trial test stage (like robot surgeons), others are already used worldwide. We defined four major trends in medical AI use. 

    Medical diagnostics

    AI-based software and machine learning technology assist doctors with running a correct diagnostic, identifying the disease, and choosing the right treatment strategy. It helps eliminate false-positive diagnoses, get more accurate data, and empower patients to run at-home screenings, thus increasing their involvement level. 

    Drug research and discovery

    Since AI’s prognostication is based on analyzing data, this technology is suitable for discovering new drugs and predicting the probability of immediate and long-term side effects. AI solutions are beneficial for the simulation of the protein or receptor response to the tested drug. 

    Below is the chart explaining the domains of AI use in the pharmaceutical industry.

    AI Future in Healthcare: Improving the Effectiveness of Medical Care - photo 3

    Workflow and administrative management

    AI takes care of workflow streamlining, hospital infrastructure and ER management, and patient flow optimization. While these processes aren’t connected to treatment, they impact the quality of care and patient experience.

    On average, a US nurse spends up to 25% of work time on regulatory and administrative tasks that algorithms can automate and complete. AI solutions free nurses, administrators, and doctors from administrative burdens like scheduling, data gathering, and information recording, allowing more time to perform activities only humans can deliver. 

    Patient support and communication

    The NLP-based (natural language processing) approach is widely used for text analysis, but it also works for AI-powered real-time chatbots used in telehealth. Their most common applications are automatic drug prescription renewal, appointment scheduling, and remote consultation with referral to proper specialists.

    The future of medical AI is looking bright if we consider the above factors. However, we can’t ignore some limitations and challenges that AI-based healthcare startups should be aware of.

    If AI is so good, why is it still not used everywhere? 

    To answer this question, we need to view it from a three-point perspective: social, economic, and legal. Let’s clarify each. 

    • Issues with social acceptance. People are suspicious of two things: whether their machine-processed data is safe and whether a robot might accidentally kill a patient during surgery. Despite numerous successful cases where robot-assisted surgeries prove to be more successful than surgeon-operated ones, people still would prefer human surgeons to do the job. However, one study shows that patients would trust AI-based solutions more in radiology and dermatology image interpretation and analysis.
    • Economic burden. The high cost of AI tech is one of the major reasons that hinder the worldwide adoption of AI-based tools and solutions. While healthcare AI does show improvement in patient outcomes, it requires substantial investment.
    • Lack of research and integrated strategy. For a government to support AI-based applications and create the necessary legal framework, it needs positive result dynamics from multiple sources of research and trials. Even though medical applications of AI have been being studied for a good while, it was only in 2020 that the World Health Organization asked member countries to develop an integrated digital health strategy. 
    • Legal perspective and privacy concerns. Since AI deals with making decisions and mimicking human behavior, it poses a question of who would be responsible for potential mishaps. Another issue concerns the regulatory framework for software when it comes to AI-powered medical devices. In addition, the developers must follow strict rules and regulations regarding data processing and safety. 
    • Lack of trained professionals. Lack of talent training also contributes to why AI in healthcare is presented locally rather than globally. 

    Despite these limitations, some solutions have gained wider practical use. 

    Five world-famous AI-powered solutions

    From identifying malignant tumors to helping doctors spot at-risk patients — these solutions may seem as though they are in the realm of sci-fi, but they’re very real. Here’s a list of our top successful AI-powered solutions.

    • Google Health/ DeepMind: Algorithm that identifies breast cancer
    • CloudMedX Health: NLP- and deep learning-based data processing solution that deals with predictive analytics
    • Turbine: cell simulation platform used for cancer studies 
    • Babylon: free smartphone app for 24/7 healthcare access that refers users to a relevant physician based on their complaints
    • Audia: AI-powered infrastructure that listens to consultations and helps doctors to identify at-risk patients

    Let’s also turn on predictive mode and see how AI should be used in healthcare in the years to come.

    Is AI the future of healthcare?

    Artificial intelligence offers various benefits for healthcare in plenty of application domains — from spotting cancerous cells to helping nurses with hospital administration. As healthcare generates more and more data, we believe that clinical data processing will remain the major application of AI. And here, we have other valuable insights.

    • Increase of remote medical assistants. With telehealth being finally accepted, distant consultations will become more common. This will cover the medical needs of more people worldwide and help foresee disease outbreaks. 
    • Integrated digital health. Since there’s an ongoing discussion on institutionalizing and integrating digital health, AI specialists need to keep secure interoperability in mind.
    • More data, more accuracy, more safety. Since machine learning improves when it gets new information, developers must ensure that such data complies with data processing regulations. 

    Policymakers are slowly but surely giving a green light for stakeholders to implement AI tools and software on a larger scale, making this niche attractive for investors. Contact us today if you want to see your product in the list of top AI solutions in healthcare 2022! Our skilled team is ready to develop AI-powered tools that will benefit your business — and humanity.

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