Dra. Joana Avelar Robledo1; Dr. Eymard Hernández López2; Dr. Leonardo David Herrera Zuñiga3
1Universidad Autónoma Metropolitana, Departamento de Química; 2Tecnológico de Estudios superiores del Oriente del Estado de Mexico, Posgrado en Ingeniería Industrial;3Universidad Autónoma Metropolitana, Departamento de Química.
Artificial Intelligence (AI) has rapidly transformed the healthcare landscape in recent years, revolutionizing everything from early disease detection to treatment personalization. This advancement has been driven by disruptive technologies like Artificial Intelligence and Machine Learning (ML), which offer significant potential in cost-effectiveness analysis and the optimization of models in health outcomes, economics, and medicine research.
In the dynamic AI market in healthcare, remarkable growth is projected, reaching USD 102.7 billion by 2028 with an annual growth rate of 47.6%. This phenomenon has led to the creation of over 8000 companies worldwide by 2023. The application of Machine Learning has created a more sustainable economy in the sector, minimizing costs, maximizing profits, facilitating patient-centered care, and improving the quality of life.
In a context where many companies face crises, ML emerges as a key solution. The pharmaceutical industry, for instance, grapples with patenting challenges and the generation of new drugs, and ML methodologies support the discovery of new treatments, therapy hybridization, and process acceleration, exemplified by SYNSIGHT in France or BenevolentAI collaborating with AstraZeneca and Merck in the United States.
In the realm of public health, Markov/clustering methodologies have proven effective in cost-effectiveness studies implemented by the Iranian government. These methodologies allow for the institutionalization of effective models in primary healthcare, countering economic imbalances caused by high rates of non-communicable diseases and increasing the population's quality life years by 2.3.
In the health insurance sector, the use of Deep Learning, according to studies from the Berlin Institute for Medical Systems Biology, has enabled the generation of adjusted cost models for personalized policies, fraud prevention, and profit maximization. These models consider a multitude of factors, from medication to medical procedures, providing a holistic view for accurate assessment.
Medical imaging has experienced notable benefits by integrating Machine Learning into early diagnostics. Massive image analysis facilitates early disease detection, improves the patient experience, and lightens the workload of healthcare professionals through updated medical devices that implement ML in their operations.
AI has also revolutionized systematic information retrieval in the sector, reducing search time by 90% compared to traditional methods, according to studies in The Lancet. The implementation of ML algorithms has enhanced the sensitivity and specificity of this search, providing more accurate and efficient results.
In conclusion, the convergence of Artificial Intelligence and Health Economics and Outcomes Research (HEOR) redefines the landscape of healthcare and research. On this exciting journey towards a more efficient, sustainable, and patient-centered future, we invite you to collaborate with us at HS-Pharmacoeconomic Studies. Together, we can explore new frontiers, drive innovation, and contribute to the ongoing advancement of global health. Join us at the forefront of the AI-driven healthcare revolution.
Let's make the future of healthcare even brighter together!