The integration of artificial intelligence (AI) into health care delivery has the potential to transform the industry, both efficiently and expediently. But it also raises ethical, regulatory, and safety concerns of the benefits and risks associated with AI in health care, with a focus on issues like biases, transparency, data privacy, and safety. Proactive measures are required to ensure that AI technologies are developed and deployed responsibly, imposing an acceptable balance between innovation and the safeguarding of patient well-being.
This article contributes to ongoing discussions in AI governance and the effective strategies guiding the responsible and impactful adoption of AI technologies in health care. By examining current trends and challenges, this review aims to arrange some groundwork for advancing AI’s role in enhancing health care delivery, improving patient outcomes, and supporting health care systems.
This discussion presents a narrative review, and a personal clinical experience aimed at exploring the multifaceted role of AI in health care. By synthesizing existing literature, this discourse aims to provide insights into the diverse applications of AI, its associated benefits, and the ethical/ regulatory considerations surrounding the integration into clinical practice. This review aims to facilitate informed decision-making among health care professionals and utilize the benefits of AI while navigating potential risks within the health care landscape.
Technical Portion
In a recent review article in Future Healthcare Today 1, the technological advancement of AI holds promises for revolutionizing patient care and administrative operations by leveraging vast longitudinal patient data, applying rapid data gathering and synchronously applying patient management. AI encompasses a spectrum of technologies, including machine learning (ML), natural language processing (NLP), rule-based expert systems (RBES), physical robots, and robotic process automation, each offering unique capabilities from predictive modeling. The integration of AI into health care affords focused diagnostic accuracy, informed decision-making, and optimized treatment planning, potentially reducing medical errors and improving patient outcomes.
However, AI adoption in health care is accompanied by several significant ethical and regulatory challenges. Concerns range from protecting patient data and privacy in addressing algorithmic biases that perpetuate disparities in health care outcomes. The regulatory landscape is evolving with technological advancements, aiming to establish robust governance frameworks that ensure the responsible use of AI in healthcare delivery. Additionally, the advent of large language models, exemplified by newer models like BERT (Bidirectional Encoder Representations from Transformers), GPT (Generative Pre-trained Transformer), have further expanded the capabilities of AI in health care.2 These models leverage vast amounts of text data to learn rich representations of language, enabling tasks ranging from clinical documentation improvement to automated summarization of medical literature.
Risks and Diseases
AI can play an impactful role by predicting which patients are at high risk of readmission based on their medical history, current condition, and other variables such as lifestyle and social determinants of health.
For instance, an AI model can analyze electronic health records (EHRs) and identify patients who are likely to be readmitted within 30 days. This enables healthcare providers to intervene proactively, thus mitigating the risk of readmission. For example, Google collaborates with health delivery networks to develop prediction models that alert clinicians of high-risk illnesses like sepsis and heart failure. ML models can also be used to forecast populations at risk of specific diseases or accidents.3 In addition, AI algorithms, such as effective learning, aid in disease classification and enable more personalized care.
Prevention of Diseases
AI can play a significant role in the prevention and control of certain social diseases. By analyzing publicly available social media data, AI can predict prevalence of several socially mediated diseases (infectious disease occurrence and propagation) and notify authorities and patients alike. AI can also analyze trends in web data to reduce the stigma associated with social disease prevention and care and identify and flag truths and misinformation.
Data Processing
Data charting forms were developed and refined based on the narrative review research question. The forms included fields for recording data such as the objective of each paper, institutional affiliations of authors, publication year, country of the first and corresponding authors, conflict of interest disclosures, health context of interest, AI applications or technologies discussed, ethical concepts, issues or implications raised, reference to global health, and recommendations for future research, policy, or practice.
Potential Surgical Improvement
AI has made significant advancements in surgical procedures. Robotic surgery, such as colorectal, gynecologic, prostate, and oral and maxillofacial surgery, enhances surgical precision and predictability. Shared tele-surgical techniques driven by AI enable remote surgery and provide better supervision of surgeons. AI-powered surgical mentorship allows skilled surgeons to offer real-time advice and guidance to other surgeons during procedures, improving surgical outcomes.
The Economics
AI has demonstrated superior accuracy and speed in analyzing medical images, such as CT scans in stroke management, leading to the early detection and treatment delivery for these time-sensitive diseases. By enabling prompt diagnosis and action before issues escalate, AI can help reduce health care costs associated with emergent stage diagnoses. In addition, AI’s ability to process and interpret various medical tests, such as MRI scans, with high accuracy reduces the likelihood of physician errors, contributing to cost savings.
AI-powered programs can simulate and evaluate numerous potential treatments to predict their effectiveness against various diseases, optimizing the drug development process in clinical trials. By leveraging biomarker monitoring frameworks and analyzing large volumes of patient data, AI accelerates the evaluation of potential treatments, leading to significant cost savings in the development of life-saving medications.
Decision Making
AI enables better data-driven decisions within the health care system. In a digitalized health care environment, the quality of decision-making relies on the availability and accuracy of underlying data. AI can assist in decision-making by offering real-time recommendations based on clinical guidelines or advancements, reducing the likelihood of medical mistakes. For example, IBM Watson Health uses ML to provide clinical decision support and achieve a high level of agreement with physician recommendations.
Personal Experience
Conclusions
In summary, AI presents a transformative force in health care, with the potential to enhance patient care, reduce errors, and broaden medical knowledge. However, its successful integration requires adaptability; complementarity with human expertise; transparency; and a deliberate, incremental approach. AI’s impact on health care is evolutionary, not revolutionary, and collaboration between stakeholders, standardization, education, and robust policies are essential to harness its full potential while upholding patient-centric care and innovation.
References
- https://futurehealthcaretoday.com/understanding-governance-requirements-and-use-cases-ai-in-occupational-health/
- www.https://vitalflux.com/bert-va-gpt-differences-real-life/
- www.https:/www.forbes.com/sites/saibala/2023/08/21/ google-is-rapidly-becoming-a-healthcare-powerhouse/
- www.https://learn.microsoft.com/en-us/dax
- www.https://pmc.ncbi.nlm.nih.gov/articles/pmc82155156
