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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM62605
Volume:
10 |
5 |
May 2026
Pages:
1-39
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of Commerce
Maharaja Agrasen college university of delhi
Department of Commerce
Maharaja Agrasen college university of delhi
The intersection of artificial intelligence and health care is one of the most radical technological revolutions of the twenty-first century, completely changing the paradigm of diagnostics, treatment procedure, and the overall structure of the healthcare delivery system across the world. This crossroads did not emerge as a spontaneous arrival but through decades of gradual technological progress, the changes in computational abilities, and an ever-digitized healthcare system that produces enormous amounts of data, which can be analyzed by algorithms. The modern environment of AI-powered healthcare is built on the groundwork of the contributions made in the 1970s and 1980s when the first ideas of expert systems that can simulate clinical decision-making were developed by computer scientists and medical professionals. Artificial intelligence in medical use dates back to the primitive rule-based expert systems, like the 1970s MYCIN, developed at Stanford University, which showed that computer programs could perform at the level of an expert, in diagnosing blood infections and recommending antibiotic doses (Shortliffe, 2012). It was a seminal work that laid the conceptual basis on the fact that medical knowledge could be encoded in a computation system and be used to solve clinical problems with logical reasoning. Yet, these early systems worked in very limited scopes and were based on manually designed rules which were not resistant facing the complexity and variability of the medical practice in the real world. These constraints of the rule-based methods all became more and more evident during the 1990s and 1980s, as the medical community realized that healthcare decision-making is a matter of probabilistic thought, pattern recognition and situational consideration beyond the scope of the simple if-then logical constructions. The technological trends initiating AI advancement in healthcare accelerated dramatically at the start of the twenty-first century, driven by three convergent technological forces: the exponential growth in the computational processing power as defined by the Moore’s Law, the spread of digital health records producing unprecedented amounts of structured and unstructured medical data, and the underlying advances in machine learning algorithms: specifically, the advent of deep learning architectures inspired by neural network research dating back to the 1940s but now capable of running computationally (LeCun et al., 2015). It was a paradigmatic shift, when the use of machine learning based on data began to replace rule-based expert systems, the AI systems started to find patterns and relationships in medical data without explicit programming, learning from examples rather than following predetermined rules. The scope of modern healthcare AI applications is truly impressive and covers numerous clinical and non-clinical areas. Convolutional neural networks have also been shown to be able to interpret medical imaging with human-level or better accuracy in radiology, pathology, dermatology, and ophthalmology, with landmark studies demonstrating AI systems at radiologist-level accuracy in diagnosing pneumonia through chest X-rays, predicting diabetic retinopathy through fundus photographs, and classifying skin lesions with dermatologist-level accuracy (Esteva et al., 2017; Rajpurkar et al., 2017). The proposed predictive analytics based on the use of data on electronic health records can help in identifying patients at risk of sepsis, hospital readmission, and disease advancement earlier, which might help alter the current reactive healthcare paradigm toward the preventive paradigm. Natural language processing systems can identify clinical information in unstructured physician notes, radiology reports, and medical literature, and chatbots can assist the initial evaluation of symptoms and triage a patient. Surgical robots with AI capabilities increase the accuracy of surgical procedures, minimize the complications of the operation and allow conducting a minimum of invasive surgery. Machine learning is increasingly becoming part of drug discovery pipelines to discover therapeutic targets, predict molecular properties, and optimize the design of clinical trials, which could shorten discovery timelines and cut the cost of pharmaceutical R&D (Chen et al., 2018). Although these technological milestones exist and there is a significant amount of hype about the importance of artificial intelligence in helping to solve long-standing issues in healthcare, such as clinical burnout, misdiagnoses, healthcare access inequalities, and rising healthcare costs, the adoption of artificial intelligence in healthcare delivery faces complex barriers on both technical, ethical, regulatory, financial, and sociocultural levels. The issue domain that this dissertation will discuss is the multi-layered nature of the opportunities and challenges that the introduction of AI technology presents the modern healthcare system through an interdisciplinary lens that includes technological, clinical, ethical, and commercial viewpoints that would be relevant to the commerce students becoming acquainted with the new field of study.
KEYWORDS
AI is the most impactful technological advancement in the healthcare sector since the formalisation of evidence-based medicine is not that its potential is fulfilled, but it is inexhaustible, and its consequences are complete. The evidence examined in this dissertation proves that AI will transform the diagnosis, therapeutics, drug development, healthcare operations, and the basic economics of healthcare delivery of all jurisdictions and care settings. It is no longer whether AI is going to revolutionize healthcare, but rather whether this revolution will be fair, commercially viable, and really helpful throughout the entire range of human health imperative. This is a time when businesspeople and managerial practitioners should be entrepreneurial and humble in structure. The market potential is enormous--yet so are the organisational, regulatory, and ethical issues that define the success and failure of which business ventures. The timeline of healthcare AI is already filled with cautionary stories of over-engineered implementations, released without sufficient clinical validation, no reimbursement routes, no real implementation assistance and no equity aspects- each failure costing capital, trust and time to the actual achievement of true clinical worth. The businesses, which stand to create sustained business payoffs, would be those that see severe clinical validation, transparent equity accounting, extended reimbursement approach, and actual implementation collaboration not as compliance overheads but as the bases of competitive edge. The general call, which cuts across all the stakeholder groups, is to make sure that the unprecedented analytical capability of artificial intelligence is used not just to lower costs to payers, create profits to investors, or decrease administrative overheads in clinicians, but in fact to make healthcare more accessible, higher-quality, and more equitable to everyone who needs it. To realise this potential, there must be continued and concerted effort on the part of the technologists, clinicians, economists, ethicists, policymakers, regulators and the communities of which they are a part. This dissertation has attempted to give conceptual underpinning and evidence base that commerce-oriented scholars and practitioners can play their part of significance and responsibility towards that common pursuit.
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Publication Date
5/6/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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