The transformative power of AI is undeniable, revolutionising numerous industries across the globe. Interestingly, while AI has permeated various sectors, its adoption in the healthcare industry seems to lag behind. The question naturally arises: Why does this discrepancy exist?

Let’s explore the factors contributing to the comparatively slower integration of AI in healthcare and shed light on the challenges and opportunities that lie ahead…

There are several reasons why AI may not be used as heavily in healthcare as it has the potential to be:


Data Challenges

AI algorithms require vast amounts of high-quality data to train and optimise their performance. In healthcare, accessing and preparing large-scale, diverse, and well-annotated datasets can be challenging due to privacy concerns, data silos, interoperability issues, and data fragmentation across various healthcare systems.

How do we address this challenge?

It is now a critical time for health providers and organisations to aggressively adopt standardised and open frameworks for data and interoperability in our care systems. This means ensuring that health data which is collected anywhere is done so in a simple, standard and accessible way – paving the way for that data to be more effectively utilised, scrutinised and securely tooled towards improving generative and reactive AI models in the future.


Regulatory and Ethical Considerations

Healthcare is a highly regulated industry, and there are strict regulations in place to ensure patient safety, privacy, and ethical standards. Integrating AI into healthcare requires navigating complex regulatory frameworks and addressing concerns related to algorithm transparency, accountability, bias, and patient consent.

How do we address this challenge?

In the “grand scheme of things”, modern AI and Machine Learning is still in its infancy – and legal systems around the world are still testing the waters to effective regulation and oversight. By ensuring that all of us who are active in the Digital Healthcare space actively contribute to the dialogue around technology, AI and Healthcare, we can make sure that the right considerations and deliberations are made to both protect patients and enable future outcomes.


Implementation and Integration Complexities:

Integrating AI systems into existing healthcare infrastructure can be complex and time-consuming. It requires coordination among multiple stakeholders, including healthcare providers, IT departments, data scientists, and regulatory bodies. Overcoming technical challenges, ensuring system interoperability, and addressing cybersecurity risks are crucial steps in successful AI implementation.

How do we address this challenge?

IT and Systems Modernisation is critical in healthcare. Too many organisations still rely on older toolchains, methodologies and frameworks to procurement and management of their technical stack. As a first step, every business in the Healthcare sector needs to ensure their supply chain for technology is able to work in cloud, mobile and ML spaces seamlessly and frictionlessly.


Lack of Standardization

The field of AI in healthcare is still evolving, and there is a lack of standardised protocols, guidelines, and best practices. This can lead to variations in AI development, validation, and deployment, making it difficult to compare and replicate results across different settings.

How do we address this challenge?

A little competition is a good thing, but silos everywhere are damaging. By embracing open connected standards like HL7, FHIR, and the data documentation approaches put forward by the PRSB, organisations in our space can prepare ourselves for the future today – ensuring that modern patters and platforms are ready and able to access and utilise existing data and process stores.


Trust and Adoption Barriers

Acceptance and trust in AI technologies among healthcare professionals, patients, and policymakers are essential for widespread adoption. Addressing concerns related to job displacement, liability, accountability, and the impact on the doctor-patient relationship is crucial for fostering confidence and encouraging the integration of AI in healthcare.

How do we address this challenge?

Every change rests on three pillars – People, Processes, and Tools. This challenge very much sits in the first “people” pillar. There’s only so far you can go to technically prepare for adoption; for every initiative in architecture and systems integration, there must be communication and training opportunities to personally prepare the healthcare workforce for understanding the changes on the horizon.

Despite these challenges, there is a growing recognition of the potential of AI in healthcare, and efforts are being made to overcome these barriers. Ongoing research, collaboration between stakeholders, regulatory advancements, and innovations in data sharing and interoperability are paving the way for wider adoption of AI in healthcare, with the ultimate goal of improving patient outcomes, reducing costs, and transforming healthcare delivery.