Use of Artificial Intelligence (AI) In Healthcare & Medicine Is Booming!
Today, the use of AI in healthcare and the medical field is on an advanced level and it continues. Artificial Intelligence (AI) is generally known for its ability to have devices perform tasks related to the human mind — such as problem-solving. However, what is less known is how AI has been used within particular industries, like healthcare.
The healthcare industry continues to develop as machine learning, and AI technology becomes more widespread. It had been reported that spending on AI in healthcare is estimated to grow at a yearly 48 percent between 2017 and 2023.
Artificial Intelligence (AI) in Healthcare
Machine learning can offer data-driven clinical decision support (CDS) to doctors and hospital employees — shaping the way for increased revenue capability. Machine learning, a subset of AI made to recognize patterns, uses data and algorithms to offer automated insights to health care experts.
Examples of AI in Healthcare and Medicine
AI can improve health by fostering preventative medication and new drug discovery. Two examples of how AI is affecting health include:
- IBM Watson’s capability to pinpoint remedies for cancer patients and
- Google Cloud’s Healthcare app makes it easier for health corporations to collect, store, and access info.
It was reported that researchers at the University of North Carolina Lineberger Comprehensive Cancer Center practiced IBM Watson’s Genomic product to determine special treatments for more than 1,000 patients. The product made extensive data analysis to discover treatment options for people with tumors showing ancestral abnormalities.
Similarly, Google’s Cloud Healthcare application programming interface (API) covers CDS offerings and other AI solutions that help doctors make more knowledgeable clinical decisions. AI used in Google Cloud takes data from customers’ electronic health records through machine learning — producing insights for health care specialists to make better clinical decisions.
Google worked with the University of Chicago and the University of California, Stanford University, to make an AI system that estimates the results of hospital visits. This acts as a means to prevent readmissions and shorten the period patients have been kept in hospitals.
Benefits, Issues, Risks & Ethics of AI in Healthcare
Integrating AI in healthcare ecosystem allows for many benefits, such as automating tasks and assessing large patient data sets to provide better health faster and lower.
According to news reports, 30% of healthcare costs are related to administrative duties. AI can automate some of these tasks, such as pre-authorizing insurance, following-up on outstanding debts, and keeping records to ease healthcare professionals’ workload and ultimately save money.
AI can analyze large data sets — pulling together individual insights and leading to predictive analysis. Quickly obtaining patient insights enables the health ecosystem to detect key areas of patient care that need improvement.
Wearable health care technology uses AI to better serve patients. Software that utilizes AI, like smartwatches and FitBits, can analyze data to alert users and their health care professionals on possible health problems and hazards. Assessing an individual’s own health through technology relieves professionals’ workload and prevents additional hospital visits or remissions.
Like all things AI, these health care technology advancements are based on data people provide — meaning, there’s a chance of data sets including unconscious bias. Prior experiences have shown that there is a possibility of coder bias and prejudice in machine learning how to affect AI findings. In the sensitive health market, it will be critical to establish new ethics rules to tackle – and prevent – prejudice around AI.
The issue of bias in machine learning is likely to become more critical as technology spreads into vital areas like law and medicine. More individuals with no in-depth technical knowledge are tasked with deploying it. Some specialists warn that algorithmic bias is already pervasive in several industries, which virtually no one is making an attempt to recognize or correct it.
We must be clear about the training information that we are using and want hidden biases in it. Otherwise, we’re building biased systems. If someone is trying to sell you a black box system for medical decision support and do not know how it works or what data was used to train it, you need to trust it.
However, it may not always be as simple as publishing details of this information or the algorithm used. Many of the strongest emerging machine-learning techniques are so intricate and opaque in their workings that they face the careful examination. To deal with this problem, researchers are exploring ways to make these systems provide some approximation of the workings to engineers and end-users.
There’s good reason to highlight the potential for bias to creep into AI. Google is one of several large companies touting its cloud computing platforms’ AI capabilities to companies.
These cloud-based machine-learning systems are designed to be a lot simpler to use than the underlying algorithms. This can help make the technology more accessible, but it might also make it easier for bias to creep into. It’ll also be important to also provide tools and tutorials to help less experienced data engineers and scientists identify and eliminate discrimination from their training information.
How AI Is Used In Medical Diagnosis
AI in healthcare is rocking medical examination with its potential to drive radical reforms to hospital procedures. AI can process pictures and individual health records with more expediency and accuracy than humans can, reducing misdiagnosis, reducing physician workload, and enabling clinical staff to provide greater value.
While first moving hospitals are already obtaining value from AI in medical diagnosis, most US hospitals are in the very early phase of their AI transformation curve — and they risk falling behind if they do not move today.
The State of Virtual Care in the USA
The coronavirus pandemic is a watershed moment for telehealth — Or using cellular technology to deliver health care services, such as remote physician consultations and individual tracking — as patients have been required to reconsider how they seek health care.
While telehealth has been on the edge of taking off for many years, consumer use of this technology only ticked up gradually before 2020. The coronavirus pandemic has given customers the drive they need to embrace telemedicine on a broad scale — we expect adoption to keep climbing provided that the epidemic rages on.
Once outbreaks became critical in the USA, consumers started linking to telehealth: Telehealth acceptance among US adults increased 6 percent points month-over-month from February 2020 — when 11 percent of respondents reported having attempted telehealth — to March — when 17% said the same, per a poll by CivicScience. And we assume adoption to keep on climbing to reach 22 percent of US adults from June.
As many patients are being encouraged to stay home and avoid looking for non-urgent medical care in-house, suppliers, payers, and clinical research are made to restrategize to ensure their clients’ requirements are being met increasingly tending to telehealth. Hospitals and doctors’ offices have had to invest in and ramp up telemedicine services to be sure they can attain their patients.
Personal and government-sponsored payers are increasing the lists of virtual services they will reimburse clinicians for. Pharma businesses and researchers have had to restrategize the ways they conduct visits with participants.
The Digital Health Ecosystem
Healthcare stakeholders can’t delay digital transformation, and moving into innovation can sate convenience-hungry customers and handle some huge challenges pressing their bottom lines.
Convenience and personalization have become table stakes in almost every market, and customers are expanding demands for these digital-powered encounters to health care – US consumers have expressed a willingness to jump ship to care providers, which could provide them with better digital experiences.
Not only do health care incumbents that adopt digitization stand to attract mindshare with customers demanding hyper-convenient maintenance, but they can also leverage innovation to attract business and avoid overspending. For example, payers incorporate digital tools in their benefits packages to improve health outcomes and slash maintenance expenses, while health systems are turning to AI and telemedicine to combat staff shortages and ease the transition into new reimbursement models.
Medical device manufacturers and Pharma companies are betting on digital technology to branch into new revenue streams, and vendors are hitting blockchain to seal cash-draining holes in supply chains. Incumbents that don’t acknowledge risk losing market to new players stocked with digital prowess to reduce costs and stay nimble.
Major tech companies — such as Alphabet, Amazon, Apple, and Microsoft — and market digital entrants, such as American Well and Livongo, are building out their companies, encroaching on the compelling region, and rising competition. These entrants aim to lure consumers from conventional health care players by their tech-focused approaches.
And since they are less burdened by massive member populations and antiquated infrastructure, they are better positioned to roll out digital services and lure in customers. That is why we’re seeing established players plan to make the most of these newcomers offer — through acquisitions and partnerships — instead of losing chunks of the business to them.
How AI will affect patients, clinicians, and the pharmaceutical industry
In health care, the impact of AI, with natural language processing (NLP) and machine learning (ML) is altering care delivery. As is the case in other industries, it’s anticipated that these technologies will continue to progress faster during the next several decades.
Three categories for healthcare applications Using AI
Since AI finds its way into everything from our smartphones into the supply chain, healthcare applications fall into three broad groupings:
- Patient-oriented AI
- Clinician-oriented AI
- Administrative- and operational-oriented AI
The future of AI in healthcare may include tasks that range from simple to complex–everything from answering the telephone to medical record review, population health trends and analytics, therapeutic drug, and device layout, studying radiology images, making clinical diagnoses and treatment programs, as well as talking with patients.
The future of artificial intelligence in Medical Care presents:
- A healthcare-oriented summary of artificial intelligence (AI), natural language processing (NLP), and machine learning (ML)
- Present and future applications in healthcare and the impact on patients, clinicians, and the pharmaceutical sector
- Check out how the future of AI in healthcare might unfold as these technologies influence the practice of health care and medicine over the next decade
The Advantages Of Artificial Intelligence (AI) in Healthcare
From computer-aided detection (CAD) systems for analysis, individual self-service to chatbots, and image data analysis to recognize candidate molecules in drug discovery, AI is currently at work raising convenience and efficiency, reducing errors and costs, and generally making it easier for more patients to obtain the health care they require.
While ML and NLP are already being used in health care, they will become increasingly important due to their potential to:
- Boost clinician and provider productivity and quality of care
- Enhance patient involvement in their own care and enhance patient access to care
- Accelerate the speed and reduce the cost to make new pharmaceutical treatments
- Personalize treatments by leveraging analytics into mine important, previously untapped stores of non-codified clinical information.
While every AI technology can contribute significant value independently, the bigger potential lies at the synergies generated by using them together across the entire patient journey, from diagnosis to treatment, to continuing health maintenance.