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.
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 USA and China are the leading countries with the highest VC funding in medical AI.
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.
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.
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.
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.
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 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.
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.
To answer this question, we need to view it from a three-point perspective: social, economic, and legal. Let’s clarify each.
Despite these limitations, some solutions have gained wider practical use.
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.
Let’s also turn on predictive mode and see how AI should be used in healthcare in the years to come.
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.
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.
