Last Updated | September 23, 2024
Executive Summary – The Cost Of AI In Healthcare
After COVID-19, AI emerged as a key tool for developing prediction models that monitor the infection and comprehend the possibility of disease spreading. The cost of AI in healthcare went up to $6.6 billion in 2021. Before the pandemic, healthcare institutions were trying to innovate using AI, but now they have pushed their boundaries. Technology can completely transform the business in at least a few fields, including clinical research, treatment procedures, and diagnostics.
Custom AI solutions in the healthcare industry guarantee that unique market issues are solved and that businesses only pay for the services they require rather than expensive off-the-shelf items unsuitable for their needs. The article is a comprehensive guide on the cost of implementing AI in healthcare.
Exploring The Cost of AI In Healthcare
Just like any other field, healthcare is also evolving and is in constant dynamic motion. 3-D printing, gene editing and use of VR are under works but what is AI in healthcare?
Artificial intelligence has the potential to improve patient outcomes and change clinical processes in healthcare. However, using this technology in healthcare settings also presents challenges, including high costs.
But why is AI so expensive? In short, the project costs are dependent on multiple factors like development, quality of data, complexity, hardware, etc. Artificial intelligence aids doctors in accurately identifying, treating and managing patient care. Therefore, it is increasingly important to use high-end resources which in turn increase the cost of AI use in healthcare.
AI changes how doctors identify, treat, and manage patient care in the healthcare industry. Doctors can tap into a pool of information and quickly assess the data, making decision-making efficient and improve patient outcomes. Because of its ability to quickly and effectively assess large amounts of data, it has the potential to enhance decision-making, efficiency, and patient outcomes significantly.
Healthcare stakeholders are increasingly heavily focusing their investments on AI-backed healthcare applications, but they are unsure if AI is cost effective in the healthcare industry. about the question “Is AI cost effective in healthcare?”
So, let’s look at what’s the cost of artificial intelligence in healthcare. AI implementation in patient care sector often costs between $20,000 and $1,000,000. Healthcare firms may use AI to simplify processes, cut costs, and deliver quality care by working with a reputable AI software development services vendor.
Apart from these expenses of implementing software and integrating new hardware, the team composition too comes with a hefty price tag. This project requires a dedicated team including data scientists, IT professionals, compliance experts, etc., raising the cost of artificial intelligence in healthcare. If we shed light on the daily cost of filling the position, expect to spare an amount between $600 to $1500. The daily cost of filling the position ranges from $600 to $1500.
Companies typically engage a project manager or scrum master to assist communication if they want a specific project to function successfully. Depending on experience and team size, costs might range from $1200 to $4600/month.
How Much Does It Cost To Develop AI Healthcare Software?
Depending on the complexity of the software and other factors, the average cost of AI in healthcare software can range from $20,000 to $1,000,000. So what cost for AI in healthcare software is required? The average price of a complete bespoke AI system can range from $20,000 to $1,000,000. Whereas, the amount of cost of a minimal viable product (MVP) falls between $8,000 to $15,000. The myth that AI is expensive and only for large tech companies like Google, Microsoft, or Facebook is pervasive. Over the past ten years, advances in computer power, networking, and algorithms, all have contributed to the increase of AI cost in the healthcare industry. Other than that, the expense depends on the necessary level of intelligence, amount of data apps will need, and how well the algorithms must work. The cost of AI in healthcare varies depending on the necessary level of intelligence, how much data apps will need, and how well the algorithms must work. In addition to the technology, several additional factors influence how much it will cost to apply artificial intelligence in healthcare.
Comparison between AI-Integrated Healthcare VS Traditional Healthcare
What Are The Benefits of AI in Healthcare?
Let us see how IoT benefits healthcare industry in the provided list below;
1. Improved Accuracy
Getting correct information is essential for detecting and treating medical problems. Doctors and other medical professionals may use AI to accelerate and enhance crucial clinical decision-making by utilizing rapid and precise data. Improved preventative measures, financial savings, and reduced patient wait times are all achieved by producing findings more quickly and realistically.
2. Increased Efficiency
One advantage of artificial intelligence in healthcare is improved diagnostic effectiveness. Large caseloads and a lack of medical history may enhance the possibility of human error in healthcare settings. Unlike physicians, AI systems can detect and diagnose illnesses more quickly and with less risk of error. For this reason, the development cost of AI for healthcare systems increases.
3. Cost Savings
The cost of AI in Healthcare industry saves expenses in a variety of ways, including both monetary and time-related ones. Machine learning in healthcare may eliminate medical mistakes, lighten the load of time-consuming administrative work, and do jobs far more rapidly than a person might be able to.
4. Digital Consultations And Health Monitoring
Cost of AI in healthcare may be a large sum, but AI enables consumers to monitor their health while giving healthcare providers vital information. From wearable health devices like the Apple Watch and Fitbit to digital consultations via your smartphone.
The Potential For Increased Efficiency Through AI-Driven Processes
Although the use of AI has been in discussion since long, not many understand its potential in entirety and how valuable it can be to businesses. According to PWC research, a productivity boost of 40% with current AI technology can be expected by 2035.
Even though artificial intelligence is one of the hottest subjects, not everyone knows how valuable it can be to businesses. As a result, productivity can increase by 40% with current AI technology.
Here, we list a few ways businesses have benefited from the exceptional effectiveness of the true cost of AI and ROI in healthcare.
Customer Service Support
AI automation of customer data can improve quality assurance and resolve undesirable situations. Rapid product problem detection leads to quicker product development, resulting in fewer irate consumers standing in lengthy lines. It makes learning how to build a medical chatbot even more significant.
Comprehension Of Text
AI’s capacity to sift through stacks of data and find practical information is one example of its effectiveness. Even straightforward Google searches have grown less accurate as the internet grows overloaded with data due to the growing use of digital noise.
Images Generated
The production of digital material is another effective medical image analysis software. It enables the development of several photos from a single prompt, saving designers significant time creating new images.
Emerging Trends In The Use Of AI In Healthcare
Here are a few emerging trends of AI in healthcare;
1. Health Diagnostics
AI improves patient outcomes by delivering quicker diagnoses to assist healthcare choices and lower the likelihood of human mistakes. Startups utilize different data processing techniques, including machine learning in healthcare and deep learning, to expand capacity and improve radiologists’ processes.
2. Telehealth
They can help patients anywhere around the globe and deliver data-driven care thanks to AI-driven online consultations and RPM/remote patient monitoring. eHealth or m-health networks include anything from straightforward recommendation engines and platforms that track and evaluate patient health to platforms that link to specialized solutions.
3. Drug Designing
AI has made it feasible to automate chemical selection and medication creation. Peptone predicts protein traits and features using AI in conjunction with Keras and TensorFlow integration, allowing researchers to simplify protein design, identify manufacturing and characterization challenges, and find unique protein features.
The Cost of Implementing and Maintaining AI in The Healthcare System
The cost of deploying AI in healthcare can vary dramatically depending on several variables. Among the significant cost factor examples of artificial intelligence in healthcare are:
1. Infrastructure
The cost of AI in healthcare depends on the required hardware and software infrastructure to the AI systems. It entails purchasing high-performance computer assets, storage platforms, network infrastructure, and, if necessary, specialist AI hardware.
2. Integrating Existing Systems
Integrating AI systems with the present healthcare infrastructure, such as electronic health record (EHR) systems, may require modifications or updates to maintain interoperability. The price of integration might raise total costs.
3. Updating And Maintenance
AI systems require ongoing maintenance, monitoring, and updates to ensure their best performance and security. It increases total costs and covers managing the potential compatibility issues in addition to ordinary software upgrades and bug patches.
4. Development And Customization
How can AI applications in healthcare reduce costs? The cost of developing different models and algorithms especially suitable for healthcare use usage in healthcare applications might be high. It works with skilled software engineers, data scientists, and AI experts who can create and apply AI solutions.
5. Collecting Data
Data gathering and management are crucial for artificial intelligence systems to learn and make accurate predictions. Gathering large data increases the cost of AI in healthcare system. Management, cleaning and utilization requires resources like IT professionals that come at a price.
6. Regulation Adherence
Healthcare systems must abide by stringent guidelines and privacy rules when implementing AI. It could cost extra money if security measures, audits, and legal counsel are necessary to comply with rules like HIPAA or GDPR.
7. Validation And Training
AI model training often needs a sizable time and computing resource commitment. Training costs might vary depending on how complex the algorithms are and what size of the dataset is. It may be necessary to use additional resources to evaluate and test the AI models to ensure their reliability and accuracy.
3 Factors Increasing The Surge of AI in The Healthcare Sector
Reducing The Cost of Implementing AI in Healthcare While Retaining Its Maximum Benefits
The infrastructure efficiency, security, data integration, and AI management and control activities often result in higher development expenses of IoT in healthcare.
But how can AI reduce cost of care? The simplest way to reduce the cost of AI in healthcare system is to have a well-structured data ecosystem already in place. Careful planning of the venture and starting small while keeping an image in mind works. Additionally, you may always employ pre-trained baseline AI models to accelerate your project or explore the possibilities of artificial intelligence.
AiGenics’ main goal is to develop a specialized platform for mental health that enables digital cognitivist behavioral therapy an d self-meditation using various tested techniques. Hence, it aimed to meet the requirements of all the people with the help of a mobile app to practice self-meditation and try professional therapy. Users can take the evaluation without having to visit a psychologist, and thanks to AI and ML, they can access the most appropriate information depending on their results.
Transform Healthcare with AI-Driven Solutions by Folio3 Digital Health
As pioneers in digital health innovation, Folio3 equips healthcare organizations with bespoke AI solutions designed to address intricate challenges. Our mastery of advanced AI technologies enhances diagnostic precision, elevates patient care, and refines operational efficiencies. From leveraging predictive analytics to deploying AI-driven imaging systems, we empower healthcare providers to fully harness the transformative potential of artificial intelligence. Collaborate with Folio3 to redefine your healthcare ecosystem through intelligent, cost-effective solutions tailored to your institution’s evolving needs.
Final Words
We must weigh how much is AI contributes to the benefit of healthcare in comparison to the cost one has to pay for it. Examining different sectors can help you determine an estimated amount of the cost of AI in healthcare. Take it as an investment today for a fruitful tomorrow, a means to expand your business.
AI can be expensive, but the healthcare sector will benefit much from it. The moment is here to invest in AI and quicken the expansion of your business. Examining different sectors can be beneficial to determine when determining the cost of AI in healthcare. and other factors worth considering.
Frequently Asked Questions
Is AI cost-effective in healthcare?
If AI is widely used within the next five years with current technology, healthcare costs might be reduced by 5% to 10%, or $200 to $360 billion yearly.
How much money can AI save in healthcare?
According to recent research, existing AI technology has the potential to reduce yearly healthcare spending by 10%, or $360 billion.
What is the biggest danger of AI and how to deal with it?
Large volumes of personal data are frequently collected and analyzed by AI systems, creating concerns about data security and privacy. We must promote stringent data protection laws and secure data processing procedures to reduce privacy hazards.
About the Author
Ahmed Faraz Qaimkhani
With over 5 years of experience in software development, I am a skilled full-stack developer who specialises in both web and mobile application development. My expertise is in designing strong, scalable, and user-friendly applications using the MEAN/MERN stack (MongoDB, Express.js, Angular/React.js, Node.js) and mobile technologies(React Native and Ionic).
