Artificial Intelligence has revolutionized healthcare and changed the way patients interact with hospitals and wards. From lab tests, generating medical prescriptions, clinic visits, booking appointments, and more, AI makes it easy for doctors to focus on what they do - providing patients with the best form of treatment and not spending time on repetitive tasks. There are different problems machine learning models can solve, and it's the job of the AI engineer to design these solutions. AI course certifications are usually accredited by medical organizations or universities, which means graduates land attractive salary packages when landing a job.
If you've ever thought of studying artificial intelligence to pursue a career in the healthcare segment, Then it is right to start now. According to Market.us, the Global Artificial Intelligence Market was valued to be worth USD 129.28 billion in 2022. From 2023 to 2032, it is estimated to reach USD 2967.51 billion growing at a CAGR of 36.8%. In this article, we shall understand how AI impacts healthcare and how to go about studying for your certification.
Impact of AI on Healthcare
AI in healthcare offers unprecedented benefits to hospitals, clinics, and other private medical firms. For over 50 years, Natural Language Processing (NLP) has been used to analyze and translate academic texts. Everyday use of artificial intelligence is clinical documentation, where medical guides are transcribed in different languages. Neurological trauma can render patients unable to speak, and AI can be paired with Brain-Computer Interfaces (BCIs) to restore their ability to communicate. More than 50,000 patients a year suffer from spinal injuries, and AI is used to improve the quality of their lives dramatically. There are various research studies that show that AI performs tasks better than regular humans, and algorithms are helping doctors spot malignant tumors in patients during operations. AI is still a growing technology and far from being perfect in the medical domain. For now, there is an increasing number of applications that doctors are using AI to automate various medical workflows and processes. The AI sector in the healthcare industry is forecasted to reach a market valuation of USD 150 billion by 2026.
Another use of Ai is with machine learning in predictive medical modeling. This is where patient outcomes for specific treatments and diseases are calculated, showing the rates of effectiveness depending on the treatment context. Deep learning is also used in radiology, medical imaging processes, and oncology-based medical diagnostics. AI is used in the medical industry for managing patient records, claims processing, and management of revenue cycles.
Top 10 Applications of AI in Healthcare
Robot-Assisted Surgery - Robot-assisted surgery is one of the most significant applications of AI in healthcare. Cognitive robotics is used to integrate data from pre-op medical journals, and real-time clinical metrics are analyzed to guide doctors in providing greater accuracy of treatment during invasive procedures. The Great Learning artificial intelligence courses covers modules on how to develop applications based on this.
Virtual Nursing Assistants - Virtual nursing assistants answer patient queries and route concerns to doctors directly. It reduces the number of unnecessary patient visits and saves time by providing patients with the right responses to their questions. AI-based virtual nursing assistants can pull outpatient clinical data, show their last recent visit, and give recommendations about possible treatment options based on their queries. AI also helps patients book appointments in advance and schedule their hospital visits without the staff checking upon them.
Fraud Detection - Clinical fraud is a major concern and one of the biggest reasons patients lose trust in hospitals. Patient data is valuable, gets stolen, and sold in dark web markets. Hackers might try making purchases using their credit/debit card information, social security number, and other hospital details before patients detect what's wrong. AI is being used to categorize, organize, and sort through their financial data and hospital records and check for discrepancies. If anything is off, users get immediately alerted.
Dosage Error Reduction - Dosage errors are a common problem, and sometimes patients can be prescribed the wrong pharmaceuticals. AI prevents dosage errors, and there are many applications being designed for effective hospital inventory tracking and management.
There are other ways AI is applied to the healthcare industry, such as clinical trial participant identification, preliminary diagnostics, automated imaging, and cyber security. The scope for AI is enormous, and we won't be seeing a shortage of demand when it comes to hiring professionals anytime soon. Great Learning AI ML PG course prepare students for the technological challenges faced by the industry and help them get ready for a successful career. AI is not just applied to hospitals but to in-home treatments as well. You can find mobile devices connected to the IoT and the development of smart home applications which have links to the healthcare industry. From patient vitals monitoring, giving medications on time, tracking meal plans, etc., AI engineers have to keep up with the transposing demands of the healthcare industry. The good news is that the pay is totally worth it, and many graduates report a high rate of job satisfaction.
What You Will Learn in an AI HealthCare Specialization
For those who are studying AI ML PG courses or thinking of pursuing a machine learning specialization in the healthcare segment, here is what they will learn:
- How to identify medical problems using machine learning
- Analyze and understand how AI is used in medical diagnostics, research, and clinical trials
- Business acumen related to medical marketing, pharmacy prescriptions, and selling drugs
- Address patient safety concerns and use AI to respond to clinical queries
- Improve patient outcomes by recommending the best courses of medical treatment to doctors
- Deliver accurate clinical diagnostics by structuring, extracting, and visualizing raw data from multiple sources
- Reduce time taken for booking appointments and the number of clinical visits
- Build capstone healthcare projects that will grab employers' attention
Conclusion
The healthcare industry is facing an increasing demand for AI professionals who can craft tailor-made applications for hospitals. By 2050, it is expected that hospitals will be understaffed, and hospitals will have to deal with patients who have complex or critical health conditions. According to a McKinsey report, the global economy will have more than 40 million job openings for AI graduates, and medical professionals will be working alongside AI engineers to design the best possible treatment outcomes for patients.
