From what we saw in 2020, it’s no wonder that healthcare is one of the largest and fastest growing industries today. Globally, the healthcare market was valued at nearly $ 8,452 billion in 2018 and is expected to grow at an annual rate of 8.9% to nearly 8.5% $ 11,909 billion industry by 2022.
AI in healthcare is changing and improving various key processes significantly, and the potential is even more diverse and amazing. Thanks to a global pandemic, the CAGR percentage has increased significantly and, among other things, has made the urge for digital implementation in healthcare all the more urgent.
While emerging technologies made their way well before 2020, last year’s landmark event sparked the big push that those on the fence and those waiting to see how things change for others really needed. For many executives, the reluctance to digital transformation is mainly based on the following concerns:
- Ambiguity about how to initiate and proceed
- Data security concerns
- Concerns about return on investment
- Lack of technical expertise
It is understandable that the concept of “digital transformation” often sounds unusual. We know it’s important, but it’s also such a big issue that often people are overwhelmed with all of the changes that it could bring. For true digital transformation, there is a fundamental change in how an organization works.
However, there is always scope to start small, change one thing at a time, evaluate the results, and move things forward. You can look at a particular technology, think about the uses that might benefit from its attributes, and start with a small project. A number of byte-sized approaches like this can greatly simplify the process of deploying AI applications in healthcare.
When it comes to choosing a technology, artificial intelligence is all the rage when it comes to the healthcare industry. So much so that it has been dubbed the healthcare industry’s new nervous system. Artificial intelligence in healthcare is changing and the potential is even more diverse and amazing. From chatbots and computer-aided detection (CAD) for diagnosis and analysis to training, AI can help providers understand disease and better manage patient health. Because of its versatility, the AI-powered healthcare market is likely to exceed $ 34 billion by 2025.
AI has also taken its turn into the skincare industry to come up with custom cosmetics and skincare solutions for consumers. Although it is just the beginning, it promises big results. You can read more about it on Proven Skincare’s blog.
So let’s take a look at the different ways AI can drive advances in healthcare in the years to come.
Benefits of AI in Healthcare:
- Accurate diagnosis: Incomplete medical records, inefficient sequencing, and large numbers of cases can often lead to human error. However, once reports are entered into a computer, advanced machine learning algorithms can arrive at the correct diagnosis, avoiding errors and greatly improving the efficiency of medical facilities.
- Accelerated drug development: Traditional drug manufacturing methods can often be prohibitively expensive and time consuming. This is a major obstacle, especially when a pandemic is threatening the world and an urgent need to accelerate. Usually it costs around $ 2.6 billion for clinically proven drugs and only 10% of those drugs eventually make it to market. In 2007, as scientists explored the various functions of yeast, Adam, a robot, quickly went through billions of data points to determine 19 genes that make up yeast and predicted 9 new and accurate hypotheses. Adam’s companion robot Eve, who was doing her own research, found that triclosan, which is commonly found in toothpaste, can prove effective against malaria-based parasites.
These discoveries ensured the continued and growing influence of technology in the medical field and resulted in faster drug production at a fraction of the previous cost.
- Enhanced patient experience: Overcrowded healthcare facilities, growing number of reports, confusion over insurance and more make for a chaotic experience every day. AI has proven to be a savior in situations like this by quickly scanning data, generating reports, and knowing exactly where to go and who to contact patients on mobile devices. In the age of remote consulting, AI is the backbone of some of the most advanced digital solutions that not only enable connection and communication, but also accurate updates of schedules, availability of reports, scheduling and much more.
See how Pulsara was able to use digital technology to bring all major healthcare communications together on a single, intuitive platform. The solution resulted in the company achieving commendable achievements and various innovation awards.
- Data security: In healthcare, protecting sensitive patient data is paramount. Rapidly advancing AI algorithms help encrypt personal information, clinical reports, diagnostic findings and more, prevent them from being hacked, and store them securely in the cloud for patients and professionals to access anywhere.
- Robotic surgery: Complex and critical operations require the utmost care, precision and expertise. With AI-enabled robots, the number of successful operations increases. The robots are equipped with cameras, mechanical arms and surgical instruments. They can be tailored to reach any space in the human body and provide a clear, magnified view of the surgical site that is far better than what human vision can provide. These surgeries reduce pain, take significantly less time, and help patients recover more quickly.
- Remote monitoring: Connected devices can save lives by monitoring events such as heart attacks and asthma attacks in real time. Remote monitoring devices use IoT networks to connect and track activities in a human body. Data can be accessed through portable devices or mobile applications, and decisions can be made quickly using AI. The wearable technology market is expected to reach $74 billion by 2026.
- Optimized training: With AI, healthcare providers can run simulations based on a huge database of scenarios that help trainees make decisions and learn from previous responses to meet training needs.
- Risk forecast: Using Pattern Recognition to Identify Patients’ Risk of Developing a Specific Disease. Machine learning in healthcare supports timely decisions and measures with valuable insights.
- Smart health insurance: Insurance companies can use connected devices to collect health data for their underwriting and health claims and risk operations. It provides transparency between insurers and customers and eliminates fraudulent claims.
- Location tracking and alerts: Smart medical devices enable real-time alerting, tracking, and monitoring, enabling practical treatments, better accuracy, quick physician intervention, and improved outcomes of full patient care. Wheelchairs, scales, nebulizers, pumps or monitoring devices can be tracked with sensors, making it easier for staff to track them.
Electronic Health Records (EHRs)
A major and largely invisible contribution that AI can make is to automatically capture a doctor’s written or spoken notes. Spending hours manually entering data into electronic health records (EHRs) is not helpful for health professionals who are on the verge of burnout.
A recent study by researchers at the University of New Mexico, described in EHR intelligence found that 13%. Stress and burnout even reported by doctors were directly correlated with EHRs. Philip Kroth, MD, director of biomedical informatics research at UNM, noted that 40%. The clinician’s stress is related to the design and structure of the clinical process, both of which are highly correlated with the EHRs
The UNM researchers worked out with others Stanford University, the University of Minnesota, Hennepin County Medical Center, and the Centura Health System in Colorado and Texas interviewed 282 clinicians about the effects of completing the EHR on stress and burnout.
The true value comes from data obtained from doctors’ conversations with patients or from their case notes. Some AI products can “extract and then contextualize information” so clinicians can act on it.
In other breaking news on AI and healthcare, Microsoft announced the acquisition of Nuance Communications, a specialist in speech recognition software technology, with products that can transcribe and analyze voice conversations between doctors and patients.
Nuance announced Dragon Ambient last year Experience (DAX), a product that works together with EHR systems to record doctor-patient conversations and put them into context Press release by Nuance.
Nuance DAX is expected to use Dragon Medical, used by over 500,000 clinicians worldwide, to create a voice-controlled exam room environment. Nuance worked with Microsoft to add Microsoft’s cloud capabilities to the offering.
Despite these great advances, the introduction of AI in healthcare is still in its founding years. Ongoing research is constantly adding new features to the technology that will lead to major breakthroughs in various industries in the years to come. In the crucial healthcare segment, which is currently experiencing one of the fastest transitions to digital, AI and ML can go a long way, and facilities have the potential to vastly improve the customer experience, create new digital businesses, and reach research goals faster, all of which contribute to make the world a better and safer place for everyone.