Making Predictive Healthcare a Reality With AI
This AiThority.com thought leadership article is co-authored by Jim Kean (CEO Molecular You), and Ash Anwar (Data Science, Sr. Director- Molecular You).
More than two million scientific articles are published every year. This number is not static; the annual growth rate of scientific publications is approximately 4% which means that in just ten years, we will reach more than four million scientific articles being published each year. This is far more data than any human can read, process, understand, and hope to implement into a product or service. To be useful, it needs organizing, correlating, and summarizing. Further, the data in scientific publications needs to be vetted to see whether or not it has been reproducibly validated by other publications, thereby adding more credibility to discoveries.
The use of large language models (LLMs), advanced artificial intelligence (AI) systems designed to understand large amounts of data and generate human-like responses, represents a strategic solution for efficiently navigating the vast landscape of scientific literature. These models serve as sophisticated tools, enabling not only the identification of novel discoveries from the ever-expanding landscape of scientific research but also providing the validation of scientific conclusions, particularly in cases of controversy or conflicting research.
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Moreover, the integration of LLMs plays a pivotal role in delivering accurate, validated, and real-time scientific information to patients and clinicians. This ensures that decision-makers in the medical and healthcare sectors have immediate access to the most reliable and pertinent data, enhancing informed decision-making and fostering advancements in evidence-based practices. In essence, the use of LLMs in this context catalyzes elevating the efficiency and effectiveness of knowledge dissemination in the scientific and medical domains.
Consider a scenario in which you have a patient group, and a clinician observes a disease presenting in a particular manner.
Simultaneously, on the other side of the globe, an academic is conducting research, identifying pivotal factors in the early development of the same condition. The academic paper may not align with the clinical findings halfway around the world, but the artificial intelligence looking at all these things may find a unique connection, and that’s where the value-add happens.
The AI’s utility is acting somewhat like a match-maker, and a librarian, organizing in categories. That’s highly valuable to any healthcare provider looking to provide the best value-driven treatment.
The healthcare landscape is undergoing a profound transformation, marked by increasing complexity and the need for more proactive, patient-centered approaches. AI technologies are experiencing rapid expansion and evolution, presenting unprecedented opportunities for the healthcare industry. AI technology will empower healthcare professionals with the ability to access, analyze, and apply vast volumes of data to enhance patient care and deliver the highest standard of medical expertise and treatment. AI not only streamlines information retrieval but also enables healthcare professionals to stay at the forefront of medical advancements, ensuring that patients benefit from the most current and effective strategies.
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The adoption of AI is not just a technological evolution; it is a strategic imperative for healthcare professionals seeking to deliver unparalleled quality and precision in patient care.
By harnessing the power of AI, healthcare providers can become more reactive, predictive, and ultimately, more preventive in their approach. AI’s ability to analyze vast datasets, from patient histories to genetic information, enables early disease detection, often before symptoms manifest.
The adoption of AI is not just a technological evolution; it is a strategic imperative for healthcare professionals seeking to deliver unparalleled quality and precision in patient care.
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