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Back to the blogFeb 19, 2024

What to Expect with AI in Healthcare in 2024

What to Expect with AI in Healthcare in 2024

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It's a tall order, but Artificial intelligence (AI) has the potential to transform patient care, improve resource allocation, and boost operational efficiency. Advancements in key areas, from administration to diagnostics, are set to revolutionize healthcare as we know it — and change is already underway. 

How Can AI Work in Healthcare?

The potential uses of AI in healthcare are dynamic and still evolving. Based on current capabilities and priorities, we're already seeing innovation in the following key areas: 

  • Diagnosis and Treatment: The emerging paradigm of "precision medicine" relies on AI-guided analytics, enabling practitioners to consider genetic, clinical, and lifestyle data to tailor patient treatment plans. Delivering this highly personalized and integrated approach at scale simply wasn't feasible before machine learning algorithms became widely available. 

  • Medical Imaging and Diagnostics: Human interpretations of medical imaging leave a much wider margin for error compared to AI-powered analysis, and they can identify subtle anomalies. These things stand to enhance diagnostic accuracy and speed. 

  • Health Monitoring and Wearables: With the assistance of AI, wearable health devices can provide individuals and healthcare professionals with real-time insights into their health status. By detecting patterns and deviations on the fly, these devices can enable proactive interventions and improve outcomes in remote patient monitoring, like for those with chronic diseases. 

  • Drug Discovery and Development: AI algorithms can expedite the drug discovery process by analyzing biological data, genetic information, and disease pathways to identify potential targets, and they can even help predict drug interactions, further shortening the time-to-market for new medications. 

What Will AI Improve in Healthcare?

AI has the potential to not only improve patient outcomes but also reduce administrative burden and overhead costs in the process. These benefits are expected to have a major transformative impact on healthcare as we know it, fostering the shift to a more efficient and patient-centric model. 

  • Improved accuracy and efficiency in diagnosis

  • Enhanced patient outcomes and personalized care

  • Reduction in medical errors and unnecessary procedures

  • Cost savings and resource optimization

What Limitations Are Holding Back AI in Healthcare? 

While highly promising, AI does not come without a unique set of challenges and limitations. AI leaders are already working to address these shortcomings, but work must continue to ensure responsible, secure, and ethical deployment of AI tech, particularly in the healthcare space where patient privacy is paramount. 

Key challenges include: 

Healthcare professionals, data scientists, policymakers, and regulatory bodies will need to collaborate to fully address these challenges from a technical, ethical, and organizational standpoint. Until these challenges are properly mitigated, the practical application of AI in healthcare (and other industries) will be limited. 

What's Ahead for AI in Healthcare? 

With the continued development of natural language processing (NLP) alongside ever-more-efficient cloud computing to power machine learning algorithms and IoT devices, AI-driven technology will only grow more reliable and accessible. Some key advancements will likely include:

  • More powerful chatbot technologies that bring on-demand telemedicine and consulting to patients and providers, even in the most rural areas 

  • Better remote patient monitoring thanks to the continued integration of AI with Internet of Things (IoT) devices, such as wearable blood pressure and glucose monitors

  • Faster, more nuanced decision-making and diagnostics, especially in ER and urgent care settings, thanks to AI-powered integrated analytics

Case Studies of Successful AI Applications

While many weren't familiar with AI until the public launch of ChatGPT and similar tools in late 2022, market leaders have been experimenting with AI applications for years. Some examples of AI at work in healthcare include the following. 

Watson for Oncology by IBM

IBM's Watson for Oncology uses AI to help oncologists provide evidence-based treatment programs for cancer patients. IBM worked with Memorial Sloan Kettering Cancer Center to develop the platform, which analyzes vast amounts of clinical trial data, medical literature, and patient records to provide suggestions tailored to the individual. 

DeepMind's AI for Early Detection of Diabetic Retinopathy

Deepmind, a subsidiary of Alphabet (Google's parent company), developed an early detection system for diabetic retinopathy, a leading cause of vision loss for diabetics. The AI-powered system analyzes retinal scans to catch early signs of diabetic retinopathy, enabling early intervention to help preserve vision quality.

AI-Powered Surgical Robots

The da Vinci Surgical System is one of many AI-powered surgical robots already being employed to conduct minimally invasive surgeries, providing greater accuracy and control to the surgeons they assist. In the future, these robots could conduct extremely complex and risky procedures with incredible precision, improving patient outcomes. 


The integration of AI in healthcare has already begun improving diagnostics and streamlining processes, and this is only the beginning. With continued advancement, AI tech will shift towards more patient-centric healthcare delivered with greater global accessibility and significantly reduced overhead. 

While continued efforts are needed to improve interpretability and address biases, AI-powered tech quickly becomes a cornerstone in modern healthcare. Today's healthcare organizations can poise themselves to keep up with the evolution by investing in AI-ready systems capable of leveraging machine learning and integrating with other institutions and platforms to usher in a new era of healthcare. 

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