AI solutions are transforming the way healthcare is delivered around the world, ultimately helping to reduce healthcare costs while improving patient outcomes.

AI is currently being used to create clinical decision support systems and extract useful insights from large sets of health data.

Although artificial intelligence is an intriguing technology that has permeated several industries, its adoption in healthcare is hampered by several factors.

These include:

1. Setup and running costs

As beneficial as AI technologies are in healthcare, setting them up and maintaining them is expensive.

These advanced technologies demand a lot of computing power and data resources.

Also, machine learning experts and other data professionals command a high salary.

To this end, many health organizations opt for cloud computing to combat the expensive demand of utilizing AI and also get a lot done with a lesser number of data professionals.

2. Professional resistance

Health professionals often have a challenge leaving their decision-making to AI technologies, being experts with little to no technological knowledge.

To combat this, health organizations would have to update their knowledge and policies to accommodate data-driven AI/ML recommendations.

3. Quality of data

The benefits of adopting AI technologies in healthcare can only be tapped with the use of quality data.

The use of data of poor quality can result in inadequate insights and false predictions and drive poor decisions in healthcare.

There are currently several problems with the quality of health data, including missing data, duplication, heterogeneity, and fragmentation in silos.

Collated data first has to be representative of the population and be structured and cleaned before ML algorithms are deployed.

4. Human bias

Machine learning models are designed by data experts subject to intricate human biases and hence can introduce these personal biases into their models, leading to inaccurate models yielding wrong outputs, which can harm organizations and their users if used around the world.

This is especially important as regards supervised ML models, as unsupervised ML algorithms make use of deep learning and are not set up by humans.

5. Lack of skilled professionals

There is currently a shortage of machine learning professionals and experts around the world.

AI is an advanced technology that requires special skill sets that conventional software designers do not possess.

Institutions are making efforts to train interested individuals in these advanced technologies.

6. Data Privacy

AI technologies work with big data.

Surely, collating and working with these data would infringe on current data privacy laws, which have impeded the adoption of AI.

Crafting AI-specific laws to regulate the adoption of AI will not be easy, as the processes involved, especially deep learning, are largely unexplainable.

 
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