Role of AI and Machine Learning in Healthcare Industry
“Experts believe the AI economy will hit $3.9 Trillion by 2022.” This transformation is backed up by the robust AI and machine learning tools – Generative Adversarial Networks (GAN), Deep Reinforcement Learning (DRL), and more.
MelaFind to AI-support assistants, technologies are all set up to deliver advance patient care, effective data management, and AI-based healthcare services.
Robust AI tools and its subset improve the healthcare sector, including MelaFind, caption guidance, Robotic-assisted therapy, and virtual assistants.
“Healthcare and AI follow a parallel path in the 21st century.”
Artificial Intelligence renovates:
- Global productivity
- Healthcare Technologies
- Patient care
- Healthcare admin processes
Artificial Intelligence has the potential to execute tasks better than humans. Let’s see how!
AI in Healthcare – THE CURRENT SCENARIO
Algorithms have already outclassed radiologists at identifying menacing tumors. AI even guides researches in understating the apropos clinical trials.
Does that mean AI and machine learning have already replaced human work in the healthcare industry?
No! AI hasn’t become smarter enough as of now to replace humans completely.
The healthcare sector and the knowledge associated is complex. Health check-ups, today, are not just limited to checking the patient’s temperature or asking questions.
With the amplified number of complex diseases, even technology has to get more advance.
Modern tests, complex medication processes, and restful treatment require high-end technologies for a better healthcare industry.
The complexity and rise in the amount of healthcare data have led to an increase in the use of AI.
Different AI types are employed by the healthcare sector, care providers, and life science firms. The AI in healthcare involves the treatment, recommendations, diagnosis, and all other healthcare administrative activities.
“Data Freaks are Loving Artificial Intelligence”
The Healthcare sector has countless data to handle. Timely tracking, analyzing, and sustaining the accurate data is compulsory.
This industry doesn’t just have simple data to maintain. It has millions of healthcare records to maintain, and AI aptly manages that all. AI and machine learning services in healthcare have become a must.
MACHINE LEARNING to AI – Healthcare Sector is Mounting
The first step is machine learning – an essential step towards AI – leading to smarter and successful healthcare. But don’t confuse yourself by mixing these two terms; machine learning and AI are different.
✓ AI vs. Machine Learning
Two hot buzzwords in the market right now. Both the terms have been used interchangeably, but it’s probably not the same. These terms turn up more often when topics like Analytics, Big Data are discussed. Artificial Intelligence is a larger theory of machines that carries out complex tasks in the smartest way.
Machine Learning is the application of Artificial Intelligence that allows providing easy data access to machines so that it learns to maintain everything itself. If we trust the visionaries and the futurists, AI is on the cusp of turning into a significant invention for healthcare.
Hospitals, healthcare sectors, and all connected services must brace themselves for the utmost renovation. The patient care industry is all set to evolve, indeed.
✓ Machine Learning Powerhouses are on the Way
The machine learning dynamos, including IBM and Google, have already extended their hold in the healthcare AI industry. The techniques developed are already utilized and applied to predictive analysis and pathology.
Microsoft has already tried its hands solving imaging analytics, vision issues, and some serious health problem like cancer.
Google has also come forward to explain the role of machine learning in cancer diagnosis and pathology as well. IBM is on its way to deal with genomics, population health management, and pharmaceuticals.
These machine learning powerhouses have indulged in some tough competition, coming up with different technologies to make healthcare processes simpler and more reliable.
It has been in the industry for a long time now.
Both technology and our minds have developed. The complex calculations and process management are not complex anymore. AI is capable of managing data and highly complicated tasks easier than ever before and in a much more human-like manner.
✓ CHALLENGES Imposed on Healthcare Sector Due to AI
AI has stepped into the healthcare industry already, but the AI algorithms can’t be trusted fully. Undoubtedly, the experts are trained to manage a high volume of healthcare data, but it is not everyone’s cup of tea to acquire the high-end clinical datasets.
Healthcare professionals can now easily collect an extensive amount of electronic health records, however, the data distributed among different healthcare institutes have strictly restricted access.
Besides, every healthcare applications and web solutions need to comply with the HIPAA and FDA requirements. There is a limit to every data, and it has to abide by all rules and regulations set by the officials.
Hence, who can access such highly protected data is still questioned.
At times, it is almost impossible to access such health information. So, that limits the AI and machine learning functioning in the healthcare industry. It is tough to obtain and access precise data out of thousands and even millions of files and images.
Lack of accurate & sufficient data required for development and testing AI models might confuse the algorithms, making it difficult to identify the apt information.
One wrong decision can strongly affect the level of accuracy and final results. When it comes to demographically diverse data, AI might not perform as it should.
✓ Coping with CHALLENGES Faced by Healthcare Due to AI
The technology is progressing, but since there are always two sides to the coin, it is almost impossible to avoid the challenges or the drawbacks of either Machine learning and AI.
Hence, it is better to rely on some solid solutions to elude such concerns. That means you can rely on really specific AI-based solutions to solve those strict healthcare requirements.
1. Divide Data for Different Purposes
The Healthcare sector needs to rely on a well-trained AI model. They will have to divide the available dataset based on different purposes, including training and validation. Go for an 80/20 ratio and utilize them for different healthcare purposes, based on your needs.
2. Eliminate Duplicated and Errors with Reviewing
The medical database is huge and complex, as we know. Healthcare professionals might not maintain, manage, and access such huge databases. AI has to step in, and, it is highly required to maintain, access, and avail such a huge database when needed.
Databases in the medical industry to have higher chances of duplicity and errors. The best solution for this is to review and re-process the entire data before actually designing your AI model for seamless results.
3. Pre-Trained AI Model Application
All healthcare institutions and hospitals must rely on a pre-trained AI model as a starting point. Adjust this ready-to-use model that is already trained to perform similar tasks. Even if the available data is minimum, it would be easier to adjust your model to the existing AI pre-trained model.
✓ How AI is Transforming Healthcare?
AI is driving huge advancements and innovative applications in the healthcare sector. And, the one such great advancement is Predictive Analytics.
One of the examples is the drug research and discovery that has hastened the diagnoses. The result of applying AI in healthcare sector is delivering efficient and fast healthcare processes.
Undoubtedly, AI has made a remarkable impact on the healthcare industry.
1. Specialists Utilizes AI for Quicker Diagnosis
AI results in highly accurate diagnoses by delivering quick data access. And, that’s the need of the hours for the practitioners these days. Disease and patient’s care don’t wait for anything. In that case, AI is a must-have for persistent support in allowing quicker data access for speedy diagnosis.
Without AI, the healthcare industry had to crunch terabytes of records for thorough patient diagnosis. This industry is relying on AI for crunching huge database now. Human beings are authenticating the outcomes.
2. Pharma Firms Quickens Development using AI
New findings and innovative solutions are expected in drug research utilizing AI. With that, doctors can provide high-end treatments, which eventually help pharmaceuticals to speed up the development process.
Witnessing the AI trends, it is believed that the pharmaceutical industry will flourish by 160% between 2017 and 2030.
3. AI Automates Claims Administration in Health Insurance
Health insurance companies are relying on AI to boost business functions. Claims processing tasks can be automated with AI, all credit goes to AI-based integrated automation platforms.
IAP or Integrated Automation Platforms allow easy ingestion, extraction, and analysis of the claims. It allows easy utilization of the business rules to clearly comprehend the coverage and eligibility of the patients.
4. AI Adoption can Build Trust and Education
AI requires appropriate data along with the right platform for successful results.
The objective of adopting AI in the healthcare sector should be to build trust amongst the patients, deliver better data management, and greater reliability on advanced tools for seamless outcomes. Apt data can be defined as data or information that can be represented to get apt results.
AI engines are trained to satisfy the requirements of dealing with the giant datasets along with the smaller ones.
So, both the larger and the smaller healthcare firms can adopt AI for building better trust amongst the patients while educating them to step into an advanced healthcare area.
Also, the primary motive of utilizing AI in healthcare is to impart the right knowledge to the patients and healthcare service providers while ensuring that the patients are in right and safe hands.
When all the healthcare firms deliver actual and accurate outcomes, patients are going to trust them. The technology will be trusted. Adoption to AI will advance and increase and more people will be experiencing the benefits that come with utilizing AI and connected technologies.
✓ How Valuable Can AI Make Healthcare Business?
There are countless health-related issues that experts can argue on.
- The hottest one nowadays is realizing the vitality of aligning financial incentives through APMs (Alternative Payment Models).
- The second one is to find relevant solutions for solving data interoperability hitches across the diverse health system, which are somehow restraining the overall progress of the healthcare industry.
Luckily, AI has relevant tools and technologies to settle most of these issues.
Let’s explore those opportunities that can be experienced with AI technologies considering the interoperability and APMs.
1. Alternative Payment Models or APMs
The core component of APM is likely to cut down the overall costs of the healthcare sector while improving the patient’s results.
It follows a value-based contracting, where the term value has been ill-defined most of the time. The outcomes are the plans with vague objectives – ending up healthcare service providers and patients thinking whether something valuable has been attained or not.
Even when the term ‘value’ has been properly defined, it can be challenging to assemble important documents and information for measuring the apt value of a specific program.
To define the value properly, quality has to be improved and assessed.
The final task of measuring and defining the value is based on computational optimization. AI allows integrating new and reliable patient data streams, generally involving advanced management tools that result in the well-defined value.
Healthcare institutions are somehow unhurried when it comes to adopting advanced AI technologies. Indeed, they adore those traditional information management systems, which mostly rely on processes managed by humans.
This particular sector’s growth might hinder because of this lethargic approach to adopt advanced AI techniques.
Major healthcare service providers rely on advanced AI tools now to deliver high-end results. While small scale healthcare institutions are still relying on complex, traditional, and outdated processes for managing their business.
The solution for better interoperability is to build a better interconnection between the large and small healthcare firms, healthcare organizations, and patients, and most importantly, educate every healthcare firm about how utilizing advanced AI technologies can help.
It, indeed, is the ultimate requirement for growing the healthcare industry using AI.
✓ Examples of AI ML Applications in Healthcare
Here are examples of ML application in healthcare:
1. Drug Discovery through AI & ML Techniques
Big industries are relying on Artificial Intelligence and Machine Learning. Drug discoveries involve complex processes that need AI and machine learning for proper functioning.
The biotechnology firm ‘BERG’ utilizes AI to analyze massive biological data. AI differentiates between healthy and disease cells to categorize new cancer mechanisms.
Another instance is the publication of protein structures that are related to the COVID-19 virus. The technology that is associated with this research is the ‘AlphaFold’ system.
2. Robots for Surgeries
Human surgeons are highly assisted with physical robots. The assistance extends to the entire surgical procedures that simplify highly complicated tasks.
- Robots increase the capacity to understand and navigate in the process.
- This leads to a surgical process that causes less pain with optimal and finest wound stitch.
- Surgeries are performed with minimum cuts and slits.
- Data and apt guidance based on the operations and surgeries done in the past, by both the machines and humans. It even includes the results that these surgeries produced.
- Real-time guidance and directions via virtual reality space. AI generates apt virtual space for surgeons to learn and perform the surgeries.
- Huge potential for remote surgery and telemedicine using simple processes.
AI in healthcare, including the surgeries and operations, is impeccable, resulting in some painless and stress-free outcomes.
3. Actionable Insights
Massive medical data at innumerable healthcare institutions, including clinics, nursing homes, hospitals, and labs have messy and unstructured data. Besides, the patient’s data is not simple statistical data; it is massive and contains essential information.
Sturdy and responsive AI solutions are allowing healthcare firms to connect to the congregation of patients’ databases.
AI and ML are analyzing a complicated mix of data types, including radiology, images, genomics, and much more.
Use Cases of AI in Healthcare
Case 1: Eye Surgery Conducted by Robot
One study was conducted on 379 orthopedic patients. They were assisted by AI robotic process, where complications were minimized during surgeries as compared to operations done by the humans.
Amongst the best use cases where AI has successfully treated human is when a robot performed eye surgery for the first time. The trial was conducted at Oxford’s John Radcliffe hospital, where 12 patients were involved. The patients were divided into two sections, where one half was operated normally by the doctors. The other half was happy to be treated with a robot.
Robert MacLaren, Professor of Ophthalmology at the University of Oxford, said that the trial “showed that the robot has great potential for extending the boundaries of what [they] can currently achieve.”
He even said, “next step will be to use the robotic surgical device for precise and minimally traumatic delivery of gene therapy to the retina, which will be another first-in-man achievement and is set to commence in early 2019.”
Case 2: Virtual Nursing Assistants
As per Syneos Health Communications, approximately 64 patients said that they would be comfy being around virtual nurse assistants.
Virtual nursing assistants diminish unwanted visits to hospitals and clinics. It even shrinks the burden of medical professionals.
Care Angel’s virtual nurse named ‘Angel’ is another successful example of AI and ML. Angel utilizes constant and timely checks via voice. It even uses AI technologies for better medical results. Not just that, it even manages and communicates the progress and details in real-time via relentless notifications.
Sensely, another virtual nurse from San-Fransisco raises approximately $8 million to deploy a number of virtual nurses to different healthcare providers. The nurses aim at keeping apt communication with the patients, without needing any human nurse to do any task.
Case 3: AI Supports Admin Tasks
The partnership between IBM and Cleveland Clinic is an evident example of how well can AI support administrative tasks. IBM’s Watson is used to dig huge data and assist healthcare service providers to deliver customized and effective treatment to the patients. It analyzes tons of medical information and papers utilizing natural language processing. The treatment plans are informed and delivered utilizing Watson.
Case 4: AI Image Analysis Helps Healthcare Professionals
There is another case where AI image analysis technology helped healthcare professionals in comparing 3D scans up to 1000 times quicker than ever before.
The process starts by training the algorithms. The training regarding how thousands of images will be compared and scanned, allowing significant comparisons to deliver the best results.
This is Just the Beginning
We have just stepped into the world of AI and Machine Learning. We have just realized the potential of technology in the healthcare sector. Of course, there are some recognized challenges that we still need to figure out.
The patient-related data is crucial, and it requires apt technology to secure, maintain, and deploy appropriate healthcare services. Massive effort to justify the legal and well-suited policy-making is obligatory to experience all benefits of AI in healthcare sector.
Latest posts by Amit Dua (see all)
- Emerging Artificial Intelligence and Machine Learning Trends [Updated] - May 12, 2021
- Agile Software Development – Why are Developers Adoring it? - April 21, 2021
- Global Mobile App Development Market – How is it Behaving Currently? - November 15, 2020