Before COVID-19, the value of health artificial intelligence (AI) was predicted to reach $6.6 billion in 2021. Suffice to say, the pandemic has only served to accelerate the industry’s development, with the critical need for rapid solutions. AI has become a pivotal tool for creating predictive models that track the virus and understand the likelihood of spreading. Quarter three of 2020 set a record for AI funding in healthcare. 

While healthcare organizations and startups were looking to innovate with AI before the pandemic, they are now doing so more than ever. It’s clear that the technology has the potential to revolutionize the industry in at least several areas, such as diagnostics, treatment protocols, and clinical research.

In healthcare, custom AI solutions can ensure that specific market problems are addressed, and firms only pay for what they need instead of expensive off-the-shelf products that are not fit for purpose. This article discusses how much AI costs in healthcare and why companies can benefit from a bespoke solution. 

cost of AI in healthcare

When should you build a custom AI solution?

The healthcare AI market doesn’t typically offer solutions to a specific problem. Within such a vast and dynamic industry, businesses can benefit from custom AI development services. There are three core reasons why off-the-shelf packages are not the right direction for healthcare AI.

  1. The performance capability of off-the-shelf solutions is limited. Bespoke solutions can yield greater performance than out-of-the-box packages, which is extremely important when the financial impact of an application is significant. 
  2. An off-the-shelf solution requires configuration to be fit-for-purpose. When you purchase a ready-made tool or software, it may not have all the necessary integrations for your application. For example, machine learning relies on training data to create accurate models. If your actual healthcare data does not match the training data used to build the AI software, it could improve a lot of re-training to avoid poor performance. Custom solutions can be structured around high-quality data rather than generic training data. 
  3. Off-the-shelf AI doesn’t exist. If you consider that AI is still an emerging field, fully mature off-the-shelf solutions cannot exist yet in every industry and function. New models and frameworks for AI are continually being developed, meaning what works today may not be the best solution tomorrow. A bespoke solution gives you the flexibility to keep up with the latest models and trends. 

For the healthcare industry, these three points are crucial. First, AI relies on vast amounts of data to make decisions. In healthcare, that data will be highly personal, making security, governance, and control integral to any solution. With a bespoke solution, you can be in control of the entire project. 

In the same vein, scaling, upgrading, and updating custom AI solutions can happen seamlessly as your industry grows and changes. For example, the COVID-19 pandemic likely saw the need to change processes and procedures, which could be beyond the scope of off-the-shelf products. 

The main challenge with custom AI solutions is cost. When looking to develop a bespoke solution, the costs are likely to be more than an off-the-shelf product in the short term, despite the long-term gains we’ve discussed above. When asking about artificial intelligence pricing in healthcare, there is not a single answer, and there are several aspects to consider. We will discuss this next.

Read also: Software in the healthcare industry

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Artificial intelligence pricing – how much does it cost in healthcare?

According to Analytics Insights, the cost of a complete custom AI solution can vary from US$20,000 to US$1,000,000. However, the minimum viable product (MVP) sets you back between US$8,000 and US$15,000. It’s a common misconception that AI costs a fortune and is only for the tech giants like Google, Facebook, or Microsoft. Improving computer power, connectivity, and machine learning algorithms have made it affordable to all organizations in the last decade. 

The variation in costs results from the level of intelligence required, the amount of data applications will consume, and how the algorithms need to perform. As well as the technology itself, various other considerations feed into the cost of implementing artificial intelligence in healthcare.  

What factors influence the cost of AI solutions for healthcare?

The cost of AI in healthcare, especially when it comes to bespoke solutions, is driven by several factors and needs investigation on a case-by-case basis.

The type of AI solution you are looking to build

Before starting on a custom AI journey, it’s worth researching the market to see if there happens to be an application out there that already does precisely what you need. Although it would be rare for that to be the case, there’s little point in re-inventing the wheel. 

Fully off-the-shelf solutions are not typically suitable for AI in healthcare but do offer the opportunity for a hybrid model. For example, you could use an off-the-shelf product as a base for your bespoke solution rather than attempting to build the technology from scratch. Such a framework reduces your timescales and costs. 

It is important to remember that AI is an umbrella term for many different applications. The type of application you want to build is a significant factor in the cost of the solution. For example, conducting a Google search is a form of AI as an algorithm sifts through the internet to find the best results. However, a computer vision system that spots cancerous tumors in CT scans is also AI but is far more complex and has completely different requirements. The variation in the type of solutions means costs can vary massively. 

learning model of AI in healthcare
Source: Semantic Scholar 

Read also: 10 examples of artificial intelligence in healthcare that will powerfully transform your business 

Requirements and team composition

Depending on the type of AI solution, you will require a different team composition and resources. At its very core, every project typically requires Data Scientists and Engineers. The scope of requirements will determine how many of each you need, and influence the artificial intelligence cost you can expect. The rate of these specialist resources can be anything between $550 to $1,100 per day according to their seniority levels and skillset. 

what is the pay by experience level for data scientists
 Source: Towards Data Science

Custom AI solutions will also need a software engineer to help build apps, dashboards, and interfaces for your solution integrations. The cost of filling the role is between $600 and $1500 per day. 

If you want a custom project to run smoothly, companies usually hire a project manager or scrum master to facilitate communication. Costs will vary based on experience and team size but it sits between $1200 and $4600 per month. 

In-house or agency development

There are pros and cons to having your AI and data team in-house instead of an external agency. If you take everything in-house, your team manages your AI healthcare system’s development, launching, maintenance, and updates, whereas you could let an agency do that for you. A Data Scientist earns an average salary of $94,000 per year and developers around $80,000. On top of that are recruitment and training costs which Glassdoor suggests are about $15,000 per year.  

When outsourcing to an agency, your technology partner handles the development and management of the solution. It typically costs less than in-house management because you don’t have all the in-house hiring costs. With most agencies, you pay a time & material fee, and the agency takes care of the rest. Moreover, the team is full of expertise and puts you in touch with ready-made talent without the cost of hiring them. When you have shorter projects, there is no commitment to long-term employment contracts. 

We’ll cover this in more detail later in the article.

Healthcare data

An AI platform cannot function without data. Data helps AI think and learn, accelerating the learning curve of the technology. For example, going back to our earlier case of using AI to diagnose cancer from images. To be effective, the AI is loaded with thousands or maybe millions of pictures of cancerous and non-cancerous organs. Using the data, the AI can train itself to understand what factors suggest cancer is prevalent. Unless a vast quantity of high-quality data feeds the AI software, it cannot make accurate decisions and could have catastrophic consequences such as incorrect diagnosis. Any AI technology must go through a training process before being deployed.

Any AI technology must go through a training process before being deployed
Source: Inside Big Data 

An organization that owns lots of clean, quality data will reduce the price of AI development. When that’s not the case, you will need to employ resources to cleanse and edit your data and train the relevant models for you to apply to your AI solution. Data Scientists spend around 45% of their time on data wrangling. 

Project scope

As AI projects grow, it’s more challenging to provide accurate cost estimates. In-depth AI initiatives could require multiple stages and numerous employees to work on them. For example, suppose you need to clean your data, create a strategy, develop a minimum viable product (MVP), spend time testing it, make a complete solution, and maintain the product. In that case, the project can take years to roll out. 

Rather than cutting your scope, include duration as a cost factor and budget for it accordingly. An advisor or data science partner can advise how to prioritize work. 

Read also: Benefits of predictive analytics in healthcare

Outsourced AI management vs. in-house

Earlier in this article, we briefly touched upon the cost of in-house versus outsourcing AI management. Hiring an in-house team is the more expensive option when considering salaries, recruitment, training, and benefit costs. Organizations take this route to control the project and ensure they own the intellectual property for everything involved. It’s a common misconception that external agencies won’t understand the business well enough to develop the most appropriate AI solution. 

However, outsourcing to an external technology partner is not only more cost-effective by negating many of the spendings associated with in-house staff but also far less risky. While they are not part of the business, as long as you have clear objectives from the outset, an agency partner with years of experience is incredibly adaptable. And what’s even more important, they help you make the right decisions regarding technological and strategic aspects, easing your way to your project’s success.

Also, outsourcing AI allows for more flexibility and helps you avoid struggles that come along with changing skill-related demands of your project as it goes. Tech partner adjusts the team setup depending on ongoing needs, either adding to or withholding given experts from the team, without the necessity to hire or terminate employees.

When working with an external partner, you get access to experienced people and priceless know-how. The top talent is working for you without the need for long-term hires, the competent expert helps you bring your project to life smoothly and cost-effectively, and you keep full control over your project.

When you consider employee retention and the time managers spend on recruitment, outsourcing is even more appealing, as per the graphic below.  

infographic presenting high (%) cost of new hires

Whether you decide to lead AI projects in-house or work with a technology partner, set your budget and strategy as appropriate, considering all the discussed aspects. 

Read also: AI in e-learning

The cost of implementing AI in healthcare – is artificial intelligence worth investing in?

The cost of implementing AI in healthcare depends on several factors, and the more complex the solution, the higher the price. The AI industry is expected to be worth $190 billion by 2025, with global spending on AI systems at $57 billion in 2021 already. The solutions offer immense value to the healthcare industry, such as patient prescreening, diagnosis, preventative care, drug research, and hospital efficiency. 

When healthcare companies consider AI, it’s the cost that tends to make most stakeholders resistant. Although there may be some expenses in the short term, the deliverables of AI innovation should see a return on investment (ROI) in no time. For example, natural language processing (NLP) is an AI application that delivers a significant return. 

When trying to assess the needed budget, it may also be helpful to take a look at other industries. Although (obviously) the specific needs of your project will differ, knowing the cost of implementing AI solutions in FMCG companies, online fitness businesses, or even other seemingly distanced areas can give you a broader idea about the costs of AI and aspects worth considering while planning your project.

The cost of AI can be high, but its value to the healthcare industry is revolutionary. Now is the right time to invest in AI and accelerate your company’s growth. 

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