International Journal of Computer Science and Data Engineering Journal homepage: www.sciforce.org ISSN : 3066-6813 Citation: Kakulavaram, S. R. (2024). “Intelligent Healthcare Decisions Leveraging WASPAS for Transparent AI Applications” Journal of Business Intelligence and Data Analytics, vol. 1 no. 1, pp. 1–7. doi: https://dx.doi.org/10.55124/csdb.v1i1.261 Intelligent Healthcare Decisions Leveraging WASPAS for Transparent AI Applications Sridhar Reddy Kakulavaram* Technical Project Manager, Webilent Technology Inc., United States Abstract Introduction: The use application of artiﬁcial intelligence (AI) in healthcare has changed dramatically since its early exploration in diagnosing acute abdominal pain. Today, AI enhances clinical decision-making, precision medicine, and diagnostics, particularly in visually-focused specialties like radiology and dermatology. Despite its potential, widespread adoption is hindered by concerns over transparency, especially with black-box models. Explainable AI aims to address this by improving the transparency and traceability of complex machine learning models, thereby maintaining patient trust and supporting evidence-based decision- making. Research Signiﬁcance: This research is signiﬁcant as it explores the way that artiﬁcial intelligence (AI) is changing medical practice, emphasizing explainable AI to enhance transparency and trust. By addressing challenges in complex clinical decision-making and advancing precision medicine, this study contributes to improved diagnostics and treatment. Additionally, it examines the ethical, educational, and regulatory aspects of AI integration, paving the way for safer and more eﬀective healthcare applications, ultimately beneﬁting patient care and outcomes. Methodology: Alternatives: Incineration, Autoclave, Encapsulation, Distillation, Ozonation. Evaluation Parameters: Waste residues, Process complexity, ﬁnancial proﬁt, Impact on quality of life. Result: The results show that Autoclave received the highest ranking, whereas Ozonation received the lowest ranking. Conclusion: Autoclave has the highest value for artiﬁcial intelligence and medicine according to the WASPAS approach. Keywords: Artiﬁcial Intelligence (AI), Explainable AI (XAI), Medical Applications, Transparency, Machine Learning. Research Article Mini Review Article Received date: August 07, 2024 Accepted date: August 18, 2024; Published date: August 25, 2024 *Corresponding Author: Kakulavaram, S. R. Technical Project Manager, Webilent Technology Inc, United States ., E- mail: Kakulavaram@gmail.com Copyright: © 2024 Kakulavaram, S. R. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Open Access 1 Open Access Introduction [1] e use of AI technology in surgery was initially explored by Gunn in 1976, who investigated the potential of using computer analysis to diagnose acute abdominal pain. Over the past twenty years, interest in medical AI has grown signiﬁcantly. Contemporary medicine faces the challenge of gathering, interpreting, and utilizing vast amounts of information needed to address complex clinical issues. [2 ]Explainable in the world of medicine, artiﬁcial intelligence has drawn a lot of attention. e diﬃculty of explain ability has been present since the inception of AI, with traditional AI systems oﬀering transparent and understandable methods. However, they struggled with managing real-world uncertainties. e rise of probabilistic learning improved the eﬀectiveness of AI applications but also made them more opaque. Explainable AI seeks to enhance the transparency and traceability of complex statistical machine learning models, especially deep learning (DL). However, to achieve truly explainable medicine, there is a need to move beyond explainable AI and embrace causability.[3] Advancements in artiﬁcial intelligence (AI) algorithms and increased availability of training data have recently made it possible for AI to enhance or even replace certain tasks performed by physicians. However, despite growing interest from various stakeholders, the widespread use of AI in medicine remains limited. According to many experts, a major barrier to adoption is the lack of transparency in some AI algorithms, particularly black-box models. Clinical medicine, which depends on evidence-based decision-making, requires transparency. If AI systems cannot provide medically understandable explanations and physicians cannot clearly justify their decisions, patient trust may be compromised. To overcome this transparency challenge, explainable AI has been developed. [4] Medical practice is about to undergo a transformation because to artiﬁcial intelligence. It has been investigated in a number of healthcare domains, such as natural language processing, population health, and precision medicine. e use of AI to visual tasks, or computer vision, is one ﬁeld that has received a lot of attention. Because of this, AI is especially pertinent to ﬁelds that rely heavily on visual cues, such as radiology, pathology, ophthalmology, and dermatology. Large- scale digital datasets are a major factor in the development of AI since they teach deep learning algorithms how to carry out tasks like identifying lesions in medical images.[5] Medical artiﬁcial intelligence focuses on developing AI systems that assist with diagnosing conditions and suggesting treatment options. In contrast to medical applications that rely solely on statistical or probabilistic methods, medical AI uses symbolic models that represent disease entities and their connections to patient characteristics and clinical symptoms.[6] Advancements in machine learning techniques within artiﬁcial intelligence (AI) are transforming medical practice.