AI in Healthcare: 5 Powerful Real-World Examples in?2024

No machine or robot will replace doctors (at least not anytime soon). Doctors are, and always will be, the leading force in medicine, along with the various health professionals. But healthcare professionals and researchers who embrace AI are very likely to be better positioned.

AI isn’t a distant vision anymore, it is here, and it is already making big strides in healthcare right now. This is not about any “hype”, this is about real life changing solutions.

In this article, I’ll explore 5 powerful examples of AI in the health sector, ultimately helping doctors, nurses, and researchers thrive in the future of medicine.

AI in the Healthcare Sector:?Numbers

According to MarketsandMarkets , the value of the AI in healthcare was estimated at $20.9 billion in 2024 and is projected to exceed $148 billion by 2029.?

This growth rate far outpaces the overall healthcare market, highlighting the significant shift towards AI-powered solutions.

This surge extends to research as well. A 2024 study by the National Institutes of Health (NIH) found a remarkable 400% increase in published research on AI applications in medicine since 2019.

Furthermore, a 2024 Radixweb report cites a survey estimating that 90% of hospitals will utilize AI for early diagnosis and remote patient monitoring by 2025.

These statistics confirm one thing: AI is rapidly transforming healthcare, reshaping how we deliver and experience care with its immense potential.

To understand this impact, let’s explore some powerful real-world examples of how AI is already making tangible change in healthcare:

AI in Diagnosis

Imagine receiving a life-altering diagnosis, only to find out later it was a mistake.

Misdiagnosis is a serious threat, with a CNN Health report estimating up to 371,000 deaths and 424,000 permanent disabilities annually in the US alone.

The consequences extend beyond the initial disease, impacting the entire course of treatment and potentially leading to a cycle of misdiagnoses based on inaccurate medical records.

Fortunately, AI offers a powerful solution, significantly improving the diagnostic process . It analyzes medical tests like MRI scans in seconds, eliminating delays and enabling quicker, more informed treatment decisions. Additionally, AI’s ability to analyze vast amounts of data beyond human capabilities helps identify subtle patterns and provide more accurate diagnoses, even before symptoms fully develop. For instance, AI can find hints of diabetic retinopathy based on eye images , offering a potentially life-saving head start on treatment.

Multiple tools are reaching an interesting level of maturity, including improving breast cancer screening, by Google Health by leveraging AI in interpreting mammography.

A recent study exemplifies this potential further. Researchers used AI to analyze DNA data from prostate cancer patients and made a groundbreaking discovery: the potential existence of two distinct sub-types of the disease. This is a huge deal because it could lead to more accurate diagnoses and, ultimately, more effective treatments tailored to each patient’s specific needs.

AI in Drug Discovery

Developing new drugs has forever been a very lengthy and slow process, often taking years. But AI is changing the game. Its ability to analyze vast amounts of data in record time, including chemical structures and disease info, allows for the rapid identification of promising drug candidates. This is a critical advantage for diseases desperately needing new treatments.

The potential of AI in drug discovery goes beyond theory. Companies like Insilico Medicine are already utilizing AI to develop treatments for life-threatening lung diseases. Beyond replicating traditional drug discovery methods faster, AI can also design highly targeted therapies and even discover new applications for existing medications.

Another interesting example comes from Nvidia and Recursion : I recently heard NVIDIA CEO Jensen Huang speak about the tech transformation in pharma. He said the next decade could bring one of the most significant shifts in any industry. Pharma is ready for disruption. It takes over 10 years and billions of dollars to bring a drug to market, with 90% failing in clinical trials. While tech has streamlined other industries, drug discovery has slowed and become more costly.

But now, drug discovery data and computing are at a turning point thanks to AI. It produces “intelligence” that leads to new drugs discovery.

This means scientists won’t rely solely on physical lab experiments anymore. AI models can now predict outcomes, helping scientists make better decisions on experiments and targets.

Recursion has spent years building a proprietary dataset, including images of billions of cells, amassing over 50 petabytes of data. Their automated wet lab runs nearly round the clock, optimizing data for AI training. For context, the company achieves a PhD’s worth of lab work in about 15 minutes. Now, with NVIDIA, Recursion has built pharma’s largest supercomputer.

A decade in, Recursion, with compute, data, and foundation models, is speeding up drug discovery. They lead drugs three times faster than the industry average at 2.5 times less cost.?

However, a recent Nature editorial reminds us that this is just the beginning. To fully reach AI’s potential in drug discovery, continued research and collaboration between AI researchers and pharmaceutical scientists are crucial.

AI in Genomics and DNA Segmentation

Ever wonder what your DNA really says about you? Scientists are getting closer to the answer thanks to a powerful new AI tool like SegmentNT .?

This “super-powered microscope” can meticulously analyze vast stretches of DNA, determining the function of each tiny section.

The real power lies in SegmentNT’s ability to identify regulatory regions?—?the control switches of genes?—?and even predict how a single gene might vary. This is a game-changer for medical research. By understanding how DNA variations influence health and disease, scientists can accelerate the development of personalized treatments.

SegmentNT’s versatility extends beyond human DNA. It can analyze DNA from various species, opening doors for groundbreaking discoveries in the animal kingdom.

AI in Clinical?Trials

The traditionally slow and expensive nature of clinical trials often delays access to life-saving treatments. However, with AI, the streamlining of this process is more effective at various stages .

One key area is protocol design. By analyzing vast amounts of medical data, AI helps researchers design more efficient trial protocols, reducing the risk of pursuing ineffective trials and wasting resources.

Additionally, AI can analyze past trials to build meta studies and optimize patient recruitment strategies for new studies, AI systems can analyze past clinical trials and identify ways to optimize eligibility criteria, potentially allowing more patients to participate while maintaining safety standards. This can significantly reduce the time it takes to find enough participants for a trial, accelerating the path to new treatments.

AI in Robotic?Surgery

Surgeons are no longer alone in the operating room. According to the American College of Surgeons , AIn coupled with robotics is rapidly transforming surgery, offering intelligent guidance to enhance precision, efficiency, and patient outcomes.

According to ResearchGate , AI can analyze patients’ medical history, scans, and genetic data. This allows AI to recommend the best surgical approach and even personalize treatment plans.

Not only that, but AI can also assist during surgeries . It provides real-time guidance and automates repetitive tasks. This minimizes human error and frees up the surgeon’s focus for more critical decision-making.

The Future of AI in Healthcare

The future of AI in healthcare is just beginning. As AI technology evolves, we can only expect even more literally life changing applications that will reshape the entire healthcare sector.

However, as we celebrate and embrace these strides, we must address ethical considerations like data privacy and algorithmic bias . Ensuring responsible development and implementation is a must so we can realize the full potential of AI to enjoy a healthier future for all of us.

Want to Know More?

This article is just a glimpse into the amazing potential of AI. If you’d like to explore more ways AI can be used, check out some of my other publications here:

• On Udemy?: Check my course on AI Regulations & Standards
• On Medium?: https://medium.com/@AhmedF
• On Twitter?: https://twitter.com/ahmedfessi
• On Udemy?: https://www.udemy.com/user/ahmedfessi/

Engage and leave a comment if you liked this article!