10 Ways Artificial Intelligence Can Impact the Healthcare Industry
Artificial intelligence (AI) for healthcare is slowly but surely causing an undeniable footprint on the medical landscape. With recent advancements in Digitized Data Acquisition, Machine Learning (ML), and Computing Infrastructure, AI applications are foraying into new zones that were previously considered the realm of human experts.
This blog aims to touch upon some of the recent breakthroughs in Artificial Intelligence for healthcare industry, their applications, and their advantages, and we also highlight the challenges that we might face while we progress.
1. Artificial Intelligence Drives Radiology Tool Innovation
AI algorithms can help with several radiology processes throughout the diagnostic chain. The image below is based on a simplified overview of the diagnostic system.
- Changing a radiology image into a radiology report
Here the AI-based software is a direct support tool for radiologists: images are uploaded, and reports are generated, often with quantifiable outcomes. - Radiology image from raw scanner data
Smart algorithms, such as MRI k-space, can be applied to raw data directly from the scanner. Deep imaging is another term for this. - Radiology report from raw scanner data
When we start skipping steps in the diagnostic chain, things get pretty interesting. In the future, this method could be used to avoid invasive biopsies. - Health outcomes from raw scanner data
Imagine being able to scan a patient and predict what the patient will face in the future.
Use Cases in AI Radiology
Let’s pretend we wish to address all feasible radiology problems that can be answered by extracting data from medical photos. The “space” we end up with will be dubbed “The AI in the radiology sector”. Visually, this will look something like this:
More than twice as much data as a simple “one modality base” algorithm requires. The algorithm must not only distinguish between brain tissue and non-brain tissue, but it must also determine if it is dealing with an MRI or a CT scan. Diverse organs have different forms and a variety of other characteristics, making it challenging for a single algorithm to handle many organs simultaneously.
An Analgorithm’ss objective in detecting a liver tumor is considerably different from assessing the quantity of fat in the liver.
2. Immunotherapy: A Vital Cog in the Treatment of Cancer
Machine learning algorithms should be able to combine and analyze highly complicated datasets quickly. The John Hopkins ImmunoMap is one such example. Scientists have created a map of the T-cell immune system and exposed it to a lab-grown virus for the first time. Researchers have used artificial intelligence for healthcare to identify T-cell receptor distances based on receptor sequence similarity.
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What is the John Hopkins ImmunoMap?
If any receptor sequence similarities are discovered, an alert is sounded to maintain a lookout for more.
Cancer patients responding to nivolumab had 15 different types of T-cell receptors on average, compared to just 8 or 9 in those who did not. Doctors say that the patients possessed a wide range of receptor weapons before treatment, which may have allowed the correct receptor to destroy their cancer cells. T-cells expressing those receptors increased when their immune system identified the correct receptor, resulting in a reduction in the structural diversity of their T-cell receptors.
3. Turning Selfies into Smart Diagnostic Tools
We can also uncover new ways for people to stay better informed about their health by combining improvements in AI with everyday technologies, such as smartphone cameras.
How artificial intelligence-assisted dermatological tools work?
Photographs of skin, hair, or nail concerns from different angles will be analyzed by an AI to help you find the likely matches for each condition — and suggest treatment options. The program will display dermatologist-reviewed material and answers to frequently asked questions for each matching condition and similar matching photographs from the web.
4. Artificial Intelligence and Surgery
A patient having bariatric surgery may use mobile applications and fitness trackers to measure their weight, glucose, meals, and activity, with the data being fed into their EMR. Automated analysis of all preoperative mobile and clinical data could result in a more patient-specific risk score for operational planning and valuable predictors for post-operative care. Intraoperative monitoring of many sorts of data could lead to real-time prediction and prevention of adverse outcomes.
Figure: Patient data is added to a population dataset through an integrated AI serving as a “collective surgical awareness,” which draws from population data to provide clinical decision support during individual instances.
Collecting vast volumes of operative footage and EMR data from many surgeons worldwide might use AI to boost knowledge exchange by generating a library of practices and approaches compared to results. Computer vision could capture unusual cases or anatomy in video databases, combining and integrating data from the pre-, intra-, and post-operative stages of treatment.
5. AI-Powered Health Monitoring Wearables
In the healthcare industry, wearables frequently gather, monitor, and interact with users’’ health data. It informs the user and physician about numerous health markers instantaneously.
As a sign of things to come, last year, the FDA provided marketing approval for an AI-powered device that detects mild levels of diabetic retinopathy. The device can analyze eye images and provide screening decisions without a physician’s interpretations. This will cause a tremendous increase in the number of screenings done for the currently underdiagnosed disease in the U.S. The device was tested using a group of 900 patients, and it was able to detect mild cases of retinopathy correctly 87.4% of the time, and if the symptoms were more than mild, the success rate was 89.5%. The FDA designated this AI-powered device as a “breakthrough device.” This categorization helps the company receive intensive technical guidance from the FDA during its development. The technology was reviewed under the De Novo premarket review pathway, a regulatory pathway created by the FDA for novel low-to-moderate-risk devices in spaces with no prior, legally marketed devices.
A recently developed AI network was able to detect people carrying the risk of diabetes with 85% accuracy just by using data from their smartwatch.
In the field of fitness, AI-enabled wearables are becoming increasingly popular. Smart helmets for bicyclists, smart glasses, smartwatches, fitness bands, and yoga trousers that aid with correct poses are just a few examples.
6. Improving Medical Devices using Artificial Intelligence Applications
Artificial intelligence (AI) is software that mimics human behavior, decision-making, and learning. AI-based medical equipment in the healthcare market could:
- Automate tasks, synthesize data from numerous sources and identify trends.
- Analyze and process data from wearable sensors to detect disease or identify symptoms of medical disorders in the early stages.
- Based on medical records, predict which patients are at a higher risk of disease, complications, or bad outcomes.
- Analyze massive volumes of data and track therapy efficacy.
Medical Devices with Artificial Intelligence Trends
Medical device businesses are creating AI medical gadgets that serve three key tasks.
- Chronic illness management: Artificial intelligence-enabled medical gadgets could monitor patients and give treatment or medication. Diabetes patients, for example, may wear sensors to track their blood sugar levels and deliver insulin to control and keep sugar levels in check.
- Companies using artificial intelligence in medical devices have developed medical imaging with greater image quality and clarity. These gadgets would also limit a patient’s radiation exposure.
- Internet of Things (IoT): Healthcare providers can manage data, keep patients informed, cut expenses, monitor patients, and work more effectively and efficiently.
Artificial Intelligence Guidance from the FDA for Healthcare Industry
AI, like any other technology, has flaws. At this stage, there is still the risk that medical equipment with artificial intelligence would misdiagnose a patient or give the wrong medication. Medical gadgets are also heavily regulated because they pose a risk to a person’s health and life.
Artificial intelligence (AI) or machine learning, an AI technique used to create and train software to learn from and act on data, is not fully regulated by the FDA under the existing regulatory structure. To make it a robust system that would meet FDA standards would necessitate inculcating the points given below.
- Developing the proposed regulatory framework, which includes issuing draught guidelines on a predetermined change control plan (for software’s long-term learning).
- Creating a robust set of good machine learning practices to evaluate and enhance machine learning algorithms.
- Developing a patient-centered approach, which involves giving users access to device transparency
7. What Role Can AI Play In Medical Records Management?
Many healthcare providers find EHR systems cumbersome. Aphysician’ss expertise extends beyond the clinical domain in which they operate, patient context information, and administrative process knowledge, yet EHRs rarely record or make all of it accessible.
One Medical, a concierge medical practice, built its EHR system tightly connected with its care and patient interaction methods. A data-driven cancer care service recently acquired by Roche, Flatiron Health purchased a web-based EHR for community-based oncology. Most present EHRs are meant for tiny medical practices and aren’t easily scaled or require extensive configuration.
A third, more promising alternative is applying AI to improve existing EHR systems’ flexibility and intelligence. Today, vendors such as IBM Watson, Change Healthcare, and AllScripts are releasing machine-learning systems that learn from fresh data and enable personalized care. Artificial intelligence (AI) is increasingly being used in electronic health records (EHRs) to improve data search and extraction and provide tailored treatment suggestions.
8. Role of Artificial Intelligence (AI) in Drug Development
Artificial intelligence (AI) is revolutionizing the pharmaceutical sector with ground-breaking advancements that automate operations at every stage of the drug development process. Pharmaceutical corporations are investing in AI-driven applications to change healthcare, reduce R&D costs, and broaden the scope of medication development.
The pharmaceutical industry focuses on supporting new candidates for medication development and uncovering hidden links for enhancing drug repurposing. The biopharmaceutical industry can monitor real-time data collection and improve patient outcomes thanks to mobile Health apps that leverage artificial intelligence. AI enables personalized treatment by analyzing patient databases and identifying the best therapies for them.
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AI through the drug development cycle
Identifying underdeveloped therapeutic areas for medications and routes of interest can assist pharmaceutical companies in progressing in the right direction. AI can collect data through IoT and process it using ML algorithms to understand the areas where there is scope for development.
Insights into the Strategies of Competitors providing Healthcare using AI
Pharmaceutical companies can use artificial intelligence to generate relevant insights and competitive advantage. Identifying relationships and prospective interactions between closely and distantly linked medications, pathways, genes, and targets, can be aided by research graphs created using network analytics.
Possibilities for repurposing drugs
Pharma companies may earn ROI faster by expediting drug repurposing using AI, which would otherwise take an average of 12 years for a new drug.
Clinical trial success prediction
Estimating clinical trial success is critical for allocating time, money, and resources to the most promising candidates. Using AI, companies will be able to identify the best-suited candidates early in the clinical trial process, allowing them to save money.
9. Artificial Intelligence Contributes to Diagnosis
Companies specializing in artificial intelligence (AI) are attempting to harness this profusion of data for good. We’re already seeing how AI may free humans from certain repetitive tasks and some components involved in intensive analytical processes.
The Role of Artificial Intelligence for Healthcare
PAIGE, which uses machine learning, is an intriguing technology in development. The machine-learning gadget analyses illness signs by analyzing the chemical makeup of a patient’s breath.
Medical ethicists and patients weigh in on how AI uses data.
Another thing to think about is how patients will react to getting a diagnosis from a computer. The hands-on learning, human experiences, and contacts with patients that a doctor has have radically differed from a computer’s data-driven analysis. While some people would welcome the ability of a system to access and analyze their medical records, others will be concerned about potential risks.
10. Impact of Artificial Intelligence on Clinical Trials and Research
Many critical clinical trial difficulties could be solved with AI technology combined with Big Data. In a recent study, nearly 40% of sponsors listed boosting trial efficiency through better protocol design, patient recruitment and retention, and study start-ups as ideal methods for improving the success rate in clinical trials. AI algorithms can analyze data from mobile sensors and applications, electronic medical and administrative records, and other sources to help reduce trial costs.
Advancing Clinical Trials
Individuals objective data from devices and sensors collected in real-time as they go about their daily lives has the potential to capture more therapeutically relevant insights and be used to assess and develop trial objectives, endpoints, and methods.
While patient-reported outcomes are an important component of any trial, the addition of objective data to add context to the subjective assessment can be unreliable. Machine learning and AI platforms can apply objective data by systematically organizing all the available data, filtering out invalids, and deriving insights from only that data worthy of being processed. It’s considerably more thorough and richer than clinical EHR data, and it has the potential to be more responsive to change.
Getting patients involved and learning new things
AI and ML could improve the concept of personalized medicine by identifying patients who are most likely to respond to specific medicines based on their unique traits and past treatment responses. The likelihood that a given medicine will work in clinical trials is vastly improved by developing considerably more powerful prediction models.
Payoda’s Expertise in AI and Healthcare
Payoda has seen and experienced first hand how artificial intelligence for healthcare can benefit both caregivers and patients. Even if you don’t have enough training data, our team uses multiple AI subsets and industry best practises to build powerful AI solutions for healthcare that transform care delivery.
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Authored by: Jeyakirushna Thavasuprabhatham
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