Artificial intelligence is slowly taking over all spheres of our lives. From smart search to personalized recommendations, it is revolutionizing multiple industries with a predicted $190.61 billion of market value by 2025.
In particular, algorithms are playing a massive role in improving patient outcomes and making healthcare more accessible. The AI in the healthcare market is expected to grow from USD 6.9 billion in 2021 to USD 67.4 billion by 2027. This positive trend will result in a CAGR of 46.2% from 2021 to 2027.
With that said, let’s have an overview of the AI impact in healthcare, its trends, and main applications. If you are looking to tap into this technology to innovate clinical workflows, read this article for valuable insights.
The Whys and Hows of AI in Healthcare
AI in healthcare is a broad term that encompasses the adoption of machine learning algorithms and other intelligent technologies in medical settings. The latter then lays the ground for a variety of innovative applications such as medical imaging, predictive analytics, and drug research.
In 2020, over 50 tech giants developed the first-ever ANSI-accredited standard that lays the foundation for the consistent use of AI in healthcare. This standard will set a pathway for a more efficient health care system and value-based medical services to Americans.
Therefore, automated clinical pathways are no longer a far-fetched dream.
But why make an extra effort and go for complex adoption processes? It’s time to dwell on the benefits.
Cost savings
According to Insider Intelligence, 30% of healthcare spending goes to administrative tasks. Intelligent algorithms can automate the majority of these tasks including document processing, following up on unpaid bills, and classifying documents. In this case, healthcare professionals will have more time on providing quality healthcare services, which saves both money and time for organizations.
Quick decision-making
The analytical capabilities of AI allow it to process vast amounts of information in real time. This is especially important for a scattered flow of medical data that consists of both structured and unstructured stimuli. By quickly monitoring patient insights such as EHRs and other medical data, healthcare organizations can identify key areas of improvement and act proactively.
Preventive treatment
Embedding decision-making systems also bodes well for improving patient outcomes. In particular, pattern recognition can help identify patients at risk of developing a serious illness. Predictive analytics also allows healthcare professionals to avert preventable conditions. By analyzing each patient’s data and medical history, the system will be able to identify risk factors and patients that are likely to develop a specific condition.
Improved healthcare accessibility
As a result of restricted or no healthcare access, statistics reveal large differences in average life expectancy between industrialized and developing countries. Also, a lack of trained healthcare personnel, as well as appropriately equipped healthcare centers, has an impact on treatment delivery in these areas. To encourage a more efficient healthcare ecosystem, AI can enable a digital infrastructure that allows for speedier identification of symptoms and triage of patients to the appropriate level and modality of care.
Interoperability
Artificial intelligence paired with interoperability of data complement each other and ensure seamless information sharing. This, in turn, translates into precision medicine which considers a full range of patient data for personalized treatment. Thus, AI can be used to track specific patient data more accurately and have a holistic image of all medical records.
Top Applications of AI in Healthcare
AI-enabled image analysis for radiology
Artificial intelligence has had a strong focus on medical imaging for quite a while. Thus, global AI in the Medical Imaging Market is projected to grow from $21.48 billion in 2018 to around $264.85 billion by 2026. Many believe that smart algorithms will optimize radiologists’ workflows, promote quantitative radiology, and help identify genomic markers. AI also allows for fast checking by reading an X-ray image in mere seconds, while it may take up to 90 minutes in the manual examination.
Intelligent systems are expected to eliminate invasive diagnostic procedures with a high risk of complications and patient discomforts, such as endoscopy and biopsy.
Already in 2021, the analysis of medical images based on artificial intelligence and cloud services increases the accuracy of disease detection and prediction of its development. Thus, The American College of Radiology’s Artificial Intelligence Laboratory has developed an open, scalable platform that is available for implementation and use in US specialty clinics and research institutes.
Drug Discovery
Over the past few years, artificial intelligence (AI) has entered the drug discovery field. According to PhRMA, it takes around ten years for a new medicine to see the market, while clinical trials take around six years on average. At the same time, the average cost of drug development is projected to be $2.6 billion.
AI can make complex drug development and research processes faster and more cost-effective, to lower the time it takes for a new treatment to reach a patient.
This year, a team of researchers from Singapore and China has developed MolMapNet, a new artificial intelligence (AI) tool for drug discovery. It predicts the pharmaceutical properties of drugs by analyzing representations of molecules based on human knowledge. Last year, a team from the Massachusetts Institute of Technology discovered a new antibiotic chemical using a computer model that can sift through more than 100 million compounds in a matter of days.
However, efforts to deploy sophisticated AI models to improve healthcare are impeded by a lack of data sharing by private enterprises, which stymies such achievements.
Predictive Analytics
Another use of AI in healthcare is the optimization of clinical workflows and administrative resources. Hospitals can better allocate their budget by crunching vast amounts of data about in-patient volume, types of treatments being administered in real-time, and forecasting future demand.
Also, predictive capabilities can help healthcare providers to:
- Improve chronic disease management;
- Prevent hospital readmission;
- Get assistance with medical research;
Moreover, predictive analytics can identify warning signs when the patient can deliver a severe condition. Amid the pandemic battle, predictive analytic methods also help assess the severity of COVID-19. Researchers from the American Chemical Society recently developed a blood test that uses predictive analytics to identify whether or not a person may develop severe COVID-19 symptoms.
Individualized precision medicine
Precision medicine is an innovative approach in healthcare that tailors disease prevention and treatment. To make healthcare customized, it considers differences in dynamic interaction between genes, lifestyle, behavior, and environment. But personalized therapies require huge amounts of data on different individuals, thus rendering manual data collection ineffective.
And that is where Artificial intelligence comes on stage. By quickly processing data, AI algorithms can apply learning strategies to predict diagnosis and staging.
When prescribing medication, AI-optimized therapy recommendations will lessen the likelihood of unintended repercussions for patients. Preventing unforeseen drug interactions and improving patient outcomes can also be achieved with the help of smart algorithms. Thus, this year, the FDA authorized over 25 different medications that target specific genetic sequences.
Health monitoring
Remote monitoring of someone’s well-being allows patients to check their vitals without leaving their homes. Besides wearable health trackers that monitor heart rate and activity levels, remote healthcare also takes the form of various telemedicine solutions. By using their smartphone and computer, patients can access healthcare services in a more convenient way. This is especially important for those in remote areas or areas that lack medical specialists.
According to Single care, the use of virtual care is 38 times higher than before the COVID-19 pandemic. Currently, around 13% to 17% of U.S. patient visits happen on virtual platforms as McKinsey & Company states.
Also, virtual healthcare now comes in different flavors. From patient portals to video appointments, it empowers remote collaboration between clinicians, doctors, and patients.
The Bottom Line
Artificial intelligence has truly become an indispensable tech force behind innovative health services. By making them more accessible and cost-effective, AI systems benefit both doctors and patients. Moreover, intelligence algorithms hold great potential for uncovering biomarkers for preventable conditions.
As innovation pushes the capabilities of automation and digital workforces, we’ll see even more applications of artificial intelligence in the healthcare industry.