AI in Healthcare

The role of AI in healthcare: benefits, challenges & everything in between

The market value of artificial intelligence in healthcare hit a new high in 2020 at $6.7bn. Experts in the field and tech veterans also reveal that the industry would be valued at around $8.6bn by the year 2025 and that revenue in healthcare would come from as many as 22 diverse AI-powered healthcare solutions.

As you read, tons of innovations across the globe are happening to promote healthcare services, elevate service delivery, paving the way for better disease diagnosis, and more. The time is really ripe for the AI-driven healthcare sector.

Let’s explore the benefits of AI in healthcare and simultaneously analyze the challenges involved. As we understand both, we’ll also touch upon the risks integral to the ecosystem.

The Benefits of AI in Healthcare

The Benefits Of Ai In Healthcare

Let’s start with the good things first. AI in healthcare is doing a tremendous job. It’s also accomplishing feats that no human has ever been able to – predict the onset of diseases like kidney concerns and a few more genetic disorders. To give you a better idea, here’s an extensive list:

  • Google Health has cracked the code for detecting the onset of kidney injuries days before it actually happens. The current diagnosis and healthcare services can detect injuries only after they happen but with Google Health, healthcare providers can accurately predict the onset of an injury.
  • Artificial intelligence is immensely helpful in knowledge sharing in the form of training or assisted learning. Specialized fields like radiology and ophthalmology require intense expertise, which can only be imparted by veterans to beginners or starters. With the help of AI, however, new entrants can learn about diagnosis and treatment procedures autonomously. AI is helping in democratizing knowledge here.
  • Healthcare organizations do a lot of redundant tasks on a daily basis. The entry of AI allows them to automate such tasks and spend more time on tasks that have higher priority. This is immensely beneficial in clinic or hospital management, EHR maintenance, patient monitoring, and more.
  • AI algorithms are also reducing operating expenses and maximizing output times significantly. From faster diagnosis to personalized treatment plans, AI is bringing in, efficiency at cost-effective prices.
  • Robotic applications powered by AI algorithms are being developed to assist surgeons in performing crucial operations. Dedicated AI systems ensure precision and minimize the consequences or side effects of surgeries.

Let’s discuss your AI Training Data requirement today.

The Risks & Challenges of AI in Healthcare

While the advantages of AI in healthcare, there are certain shortcomings of AI implementations as well. These are both in terms of the challenges and risks involved in their deployment. Let’s look at both in detail.

Scope of error

Whenever we talk about AI, we inherently believe them to be perfect and that they can’t make mistakes. While AI systems are trained to precisely do what they’re supposed to through algorithms and conditions, the error could stem from different other aspects and reasons. Error due to poor quality data being used for training purposes or inefficient algorithms could limit an AI module’s ability to deliver accurate results.

When this happens over time, processes and workflows that are reliant on these AI modules could consistently deliver poor results. For instance, a clinic or a hospital could have inefficiency in bed management practices despite automation, a chatbot could falsely diagnose an individual with a concern like Covid-19 or worse, miss out on diagnosing, and more.

Consistent availability of data

If the availability of quality data is a challenge, so is the consistent availability of it. AI-based healthcare modules require massive volumes of data for training purposes and healthcare is a sector, where data is fragmented across divisions and wings. You’ll find more unstructured data than structured ones in the form of pharmacy records, EHRs, data from wearables and fitness trackers, insurance records, and more.

So, there’s massive work in terms of annotating and tagging healthcare data even if they’re available for specific use cases. This fragmentation of data increases the scope of error as well.

Data Bias

AI modules are a reflection of what they learn and the algorithms behind them. If these algorithms or datasets have a bias in them, results are bound to be inclined towards specific outcomes as well. For instance, if m-health applications fail to respond to particular accents because they were not trained for them, the purpose of accessible healthcare is lost. While this is just one example, there are crucial instances that could be the line between life and death.

Privacy & cybersecurity challenges

Privacy &Amp; Cybersecurity Challenges Healthcare involves some of the most confidential pieces of information about individuals such as their personal details, diseases and concerns, blood group, allergy conditions, and more. When AI systems are used, their data is often used and shared by several wings in the healthcare sector for precise service delivery. This gives rise to privacy issues, where users are exposed to the fear of their data being used for diverse purposes. With respect to clinical trials, concepts like data de-identification come into the picture as well.

The other side of the coin is cybersecurity, where the safety and confidentiality of these datasets are of optimum importance. With exploiters triggering sophisticated attacks, healthcare data has to be safeguarded from any and all forms of breaches and compromises.

Wrapping Up

These are the challenges that need to be addressed and fixed for AI modules to be as airtight as possible. The whole point of AI implementation is to eliminate instances of fear and skepticism from operations but these challenges are currently pulling the accomplishment. One way you can overcome these challenges is, with high-quality healthcare datasets from Shaip that are free from bias and also adhere to strict regulatory guidelines.

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