AI and Machine Learning-Driven Software Development Frameworks for Healthcare Applications

Abstract

Medical sector is facing an immense change with the incorporation of Artificial Intelligence (AI) and Machine Learning (ML) technologies that are creating novel applications to enhance patients care, diagnosing them and treating them effectively. With the increased demand in the discovery of customized and data-oriented health solutions, there is a need to embrace the use of AI and ML-led approaches in software development models which will lead to the optimization of the system and the process of decision making. The present paper examines how AI and ML will contribute to the future of software development frameworks in applications in healthcare. We cover the technological progress in the AI/ML, confronting how that has been used in medical diagnostics, predictive analytics, and personalized treatment plans. The purposes of the research are to analyze the existing frameworks, define the existing difficulties in their implementation, and introduce the new framework that can enhance the quality and efficiency of healthcare applications, based on AI/ML. The study methodology entails an extensive literature review of available frameworks, reviewing case studies, and the introduction of AI/ML algorithms on healthcare data datasets. The paper qualifies features of different frameworks in regard to accuracy, scalability, and usability through experimental simulations and real-life healthcare contexts. Findings show that AI and ML-based frameworks are effective in improving the diagnostic accuracy, simplifying the management of the patient and optimizing healthcare resources. Healthcare applications with lasers can obtain quicker and more accurate outcomes with an added benefit of integrating machine learning models and AI technologies. To sum up, it is important to have AI and ML-backed software development models that will enable the development of the next-gen healthcare applications that will bring essential advances in healthcare delivery, decision making, and improved patient outcomes.

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