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Then there is the ever-present pandemic that has been threatening this planet for years. It got me thinking: can technology be used to combat all these horrible diseases and improve patient outcomes. Is artificial intelligence going to play a role in this? It appears so.
We’ve achieved another milestone in Artificial Intelligence adoption: $6.9 Billion of market value and counting.
The intelligent healthcare market will reach 67.4 Billion by 2027. The future of AI in healthcare is bright but not peaceful.
Today I will walk you through artificial intelligence in healthcare. Its main applications and limitations. These will give you a complete picture of the technology used in medical services.
The Current State of AI in Healthcare
Artificial Intelligence, which is currently a key area of IT research, promotes industrial growth. AI is today hailed as the source for breakthroughs, just like how power technology was transformed to create the Industrial Revolution.
COVID-19, a group of healthcare leaders, has increased investments in AI.
More than half of healthcare leaders believe artificial intelligence (AI), will drive innovation within their organizations over the next few years. Moreover, approximately 90% of hospitals have implemented AI strategies.
Let’s now take a look at some of the most important impacts of intelligent algorithms on medicine.
Medical Technology: Current Technological Impacts
Only a few clinical settings have accepted the application of artificial intelligence.
Because augmented medicine allows patients greater autonomy and more personalized care, they have been eagerly awaiting its deployment. Clinicians are less enthusiastic about augmented medicine because it requires fundamental changes in clinical practice.
However, there are enough AI applications to evaluate its potential.
Early Disease Detection
The early detection of a disease can make a huge difference in the prognosis for treatment. AI-driven technology can be used to increase the accuracy of cancer diagnosis in its early stages.
Machine learning algorithms can also process data from ECG, EEG, or X-ray images in order to prevent aggravation.
The American Cancer Society estimates that 1 in 2 women are misdiagnosed as having cancer because of inaccurate mammography results. There is a dire need to improve the accuracy and effectiveness of disease identification. Mammograms are examined and interpreted 30% faster and up to 99 percent accuracy using AI, which reduces the need for biopsies.
Faster Drug Discovery
Alphabet launched this year a company that uses AI to discover drugs. DeepMind, an Alphabet unit that pioneered artificial intelligence for predicting the structure of proteins, will be its foundation.
It’s not the first instance of AI-enabled medical research.
A survey by Deloitte found that 40% of drug discovery companies used AI to monitor chemical repositories looking for drug candidates in 2019. Intelligent computing is used by more than 20% to identify potential drug targets. 17% also use it for computer-assisted molecular engineering.
Healthcare Data Analytics
In recent years, the healthcare data explosion has been gaining momentum. The rapid rise in data can be attributed both to the digitalization of healthcare and the proliferation of wearables.
A single patient is responsible for approximately 80 megabytes per year of imaging and EMH data. The compound annual growth rate of data will reach 36% by 2025.
Physicians need an efficient and fast tool to understand this data flow in order to generate industry-changing insights. Predictive analytics is one such tool. AI-enabled data analytics helps to uncover hidden trends in the spread of disease. This allows for proactive and preventive treatments, which improves patient outcomes.
The Centers for Disease Control and Prevention (CDC), for example, uses analytics to forecast the next flu epidemic. They use historical data to assess the severity of future flu seasons, which allows them to make strategic decisions ahead.
Global pandemics are not isolated events. Thus, The National Minority Quality Forum has launched its COVID-19 Index. This is a tool to help leaders plan for the next wave of coronavirus.
Clinical Intelligence
Labs have conducted more than 2800 clinical trials in the last year to test life-saving medication and vaccines against the coronavirus. This large field of clinical trials was not fruitful, and it has led to misleading expectations. It’s not new news.
Ineffective preclinical planning and investigation have been a problem in the $52B markets for clinical trials. Finding patients is one of the most challenging aspects of clinical research. Many of these clinical trials, especially oncology trials, have become more complex, making it more difficult to find patients within a limited time frame.
Artificial intelligence has great potential to speed up the selection process.
Artificial intelligence can speed up the selection process by:
- Maximizing patient unification. This could be achieved by harmonizing large EMR and EHR data in different formats and levels.
- Prognosticating clinical outcomes. This is the selection of patients with a higher likelihood to achieve a clinical objective.
- Predicting the population that will be most affected by the treatment.
Personalized Care
Artificial intelligence is a key component of precision medicine. It can be used to benefit organizations in many ways. Personalized medicine could be in the form of digital solutions that enable one-on-one interactions with specialists from anywhere.
Google Play currently has over 53K apps for healthcare. They are so popular because of this. Healthcare apps offer convenience to patients. Mobile healthcare technology has made it possible for patients to save money and gain more control over their own health.
These encouraging statistics show the value of this tech boom:
- The market for mHealth apps is valued at $47.7 Billion in 2022, and it is expected to rise to $149 Billion by 2028.
- The pandemic caused a 14.3% increase in the market’s growth during 2020. This market will also see a 17-18% year-over-year increase in the next five years.
- The main economic benefit of mHealth apps is in reducing hospital costs by decreasing readmissions and lengths of stay and by assisting patients with their medication compliance.
Precision medicine is another aspect of personalization in healthcare. This innovative model of medical services offers personalized healthcare customization through medical treatments, solutions, practices, and products that are tailored to a subset of patients. Precision medicine may be based on imaging, molecular diagnostics, and analytics.
Precision medicine, however, is not possible within the traditional medical system. It requires massive amounts of data and cutting-edge functionality. These data include a large range of patient data including personal data, health records, and family history. The AI computes these data, generates insights, and empowers clinician decision-making.
What is stopping AI Transformation in Healthcare?
Machine intelligence has great potential to disrupt healthcare and make it more affordable and accessible. The adoption of AI is still in its early stages because of many industry limitations.
These include:
- Fragmented medical data is a major obstacle to automation. Effective data capture is further complicated by the combination of structured and unstructured output. 80% goes to unstructured siloed data scattered across medical systems.
- The speed at which AI is adopted will depend on a complex network of economic factors as well as ethical considerations. There are currently no standards for AI systems within healthcare. This raises concerns for both doctors and patients. Intelligent systems can’t be used in resource-poor environments, so significant investments are required.
- Privacy is another issue associated with digital transformation. Smart algorithms that consume large amounts of data increase the threat surface for cybercriminals. Additionally, sensitive information is a major concern. This means that the most effective security measures are required and the compliance with federal regulations such as HIPAA.
Conclusion
Artificial intelligence in healthcare is a long-awaited disruption. It has been around for a while. The possibilities of artificial intelligence are almost limitless. They can be used to accelerate drug discovery and at-home diagnostics. AI will see significant growth in 2022 due to the pandemic-induced crises and urgent need for automation. While AI is still in its infancy, AI will continue to revolutionize healthcare.
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