AIMultiple ResearchAIMultiple Research

Healthcare AI Consulting: In-depth Guide for 2024

From enabling personalized healthcare to accelerating drug discovery, applications of artificial intelligence (AI) are transforming the healthcare industry. However, developing and implementing a successful AI project poses significant challenges, due to data security and legacy systems, to CIOs at healthcare providers and pharmaceutical companies. This slows down AI adoption and prevents these organizations from creating a competitive edge.

Getting AI consulting services can help businesses operating in the healthcare industry overcome the challenges posed by AI development and reduce the odds of failure.

What are the challenges of applying AI in the healthcare industry?

IBM Watson for Oncology is a well-known example of an AI failure in healthcare. It is an AI-enabled advisory tool for oncologists to diagnose and treat cancer patients faster. However, the tool frequently gave erroneous advice and the project cost more than $60 million with no achievement.

Data security

Data security/privacy is one of the biggest challenges of AI projects in healthcare. Patient medical data contains highly sensitive and personally identifiable information (PII) such as:

  • Medical histories
  • Ongoing conditions
  • Social security numbers
  • Payment information

As a result, patient medical records are heavily protected by regulations such as HIPAA.

Unstructured Data

Unstructured data refers to data that cannot be categorized with existing database structures such as medical scans and handwritten prescriptions. Processing unstructured data is challenging because: 

  • There is no specific way that machines can use to understand this unstructured data
  • It comes in a variety of formats
  • Data quality can be inconsistent

It’s estimated that ~80% of medical data remains unstructured after its creation.

Legacy systems

A 2020 survey by the non-profit Healthcare Information and Management Systems Society (HIMSS) revealed that 80% of healthcare organizations use legacy systems such as outdated operating systems like Windows Server 2003 that no longer receive support from the manufacturer.

Replacing legacy systems can be challenging because it can lead to integration issues and disrupt operations. On the other hand, legacy systems constitute an obstacle for AI implementation because those systems were not developed with AI applications in mind. This can create incompatibilities between these systems and the requirements of AI platforms. For instance, legacy systems may not be able to handle large volumes of data that AI applications typically use.

Feel free to check our article on other challenges and how to overcome them.

What are the benefits of getting healthcare AI consulting services?

  • Optimize resource management: The lack of skilled data scientists and AI experts is on of the top challenge for businesses that want to adopt AI. Building an in-house team of data scientists can be costly and time-consuming since there’s a shortage in talents. Considering the specific challenges of AI applications in healthcare, getting consulting services from vendors with healthcare domain experience can save you time and money.
  • Ensure data privacy: Getting consulting services may involve sharing data with third parties which is problematic since medical data is one of the most sensitive data types. On the other hand, data privacy problems are not peculiar to third-party involvement; ~20% of healthcare employees are willing to sell patient data to unauthorized parties for as little as $500. If you lack a mature data privacy practice, a healthcare AI consultant with data privacy expertise can help you ensure the security of your data using privacy-enhancing technologies (PETs).
  • Gain insights from unstructured data: Consultants who have experience in techniques such as natural language processing (NLP) or computer vision (CV) can help healthcare providers gain insights from unstructured data such as handwritten texts or radiology images.
  • Provide suitable modernization: AI consultants may also provide healthcare specific legacy system modernization services by improving your IT systems to get the optimal performance from your AI application. They can replace outdated operating systems and applications with modern ones and help healthcare providers use cloud computing for AI applications.

What are the different types of healthcare AI consultants?

  • Tech consultants that provide healthcare AI services such as IBM and Accenture
  • Traditional consultants with healthcare AI services such as BCG Gamma and Deloitte
  • End-to-end healthcare AI solution providers. These tech companies build custom AI/ML systems that meet your business needs, in addition to AI consulting.
Deep learning platform by Positronic for biomarker discovery.
Source: Positronic

What to consider when choosing a healthcare AI consultant?

If you have decided to work with a healthcare AI consultant rather than building an in-house solution, then there are several factors you should consider before choosing a vendor:

  • Domain expertise: The consultant must have the necessary experience and expertise. Companies must analyze a vendor’s:
    • Technical expertise
    • Healthcare industry-specific expertise
    • Process-specific expertise
  • Team: You can also check team members’ backgrounds that would work on developing your solution. Knowledge of a diverse set of tools and technologies can be a sign of high-quality work.
  • References: References and case studies from past work are always a good source of information. A reputable vendor would provide a portfolio to document its previous projects and you can contact companies that worked with them.
  • Support and maintenance options: The performance of the AI application can degrade and you may need additional support after implementation. You should check the vendor’s support and maintenance model.

For a more detailed account, feel free to check our article on outsourcing best practices.

Further Reading

For more on AI consulting and outsourcing:

If you want to learn more about software and service providers in the AI landscape, check our data-driven lists of:

If you have questions about AI/ML consulting and its specific applications in the healthcare industry, feel free to contact us:

Find the Right Vendors
Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
Follow on

Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

To stay up-to-date on B2B tech & accelerate your enterprise:

Follow on

Next to Read


Your email address will not be published. All fields are required.