Opportunities and challenges associated with artificial intelligence in healthcare

Introduction

Big data processing has become a standard in healthcare. Analyzing large amounts of data was only possible for professionals in their respective fields with artificial intelligence (AI) (1). AI has become commonplace in a post-pandemic society, no matter what industry. The purpose of this paper is to discuss the pros and cons of AI in healthcare, provide real-world examples of the implementation of AI in healthcare, and present future implications associated with the integration of AI in healthcare.

“Digital technologies, machine learning algorithms, and AI are transforming medicine, medical research, and Public Health” (2). In healthcare, the streamlining of tasks, the improvement of operational efficiencies, and the simplifying of complex procedures are all assisted by AI (3). AI is a powerful tool in healthcare for patients, doctors, and researchers (4).

Integration of AI is transforming healthcare delivery toward more precise, targeted, and effective treatments (5). From as far back as the 1970s, the diagnosis and treatment of diseases as been done with AI (6). Along with the positive outcomes of AI in healthcare, there are also drawbacks to its integration. After research conducted worldwide, only a few AI applications have been integrated into clinical practice. Non-standardized medical records, limited availability of curated datasets, and stringent legal/ethical requirements to preserve patients’ privacy are a few barriers to adopting clinically-validated AI applications (1). Even with the challenges related to data quality, ethics, regulation, generalizability, and human-machine collaboration, AI still presents immense opportunities for personalized healthcare (5).

Opportunities of AI in healthcare

Improved diagnosis and treatment, ease of administrative burden, personalized patient care, and cost reduction are all opportunities to implement AI in healthcare. The main focus of adopting AI in healthcare is supporting and improving early diagnosis and prevention of various diseases. AI is also being evaluated in radiological diagnoses in the oncology specialty for using whole-body imaging, colonoscopies, and mammograms (2).

AI has also allowed the streamlining of administrative tasks in healthcare. For example, AI can automate approval processes, facilitate electronic procurement, and improve inventory management (7). Appointment scheduling, translating clinical details, and tracking patient histories are other innovations that could be done with AI (3). Insurance reviewing is one example of a specific task streamlined by AI. This is an efficient way to manage and avoid claim denials, minimizing costs. According to Drexel University (3), healthcare professionals can identify and address mistaken claims with AI before insurance companies deny them payment. This could streamline the claims process, resulting in AI saving hospital staff the time to work through the denial and resubmit the claim.

Personalized patient care is another aspect in which AI positively impacts healthcare. Leverage of AI algorithms for data integration, predictive analytics, real-time monitoring, and image recognition, diagnoses can be more accurate, and personalized treatment plans can be developed, improving overall patient outcomes (8).

AI integration can also reduce healthcare costs by lowering operational expenses (6). AI can analyze large amounts of data, optimize various processes, and deliver more personalized patient-centered care at lower costs. AI technology implementation in healthcare will reduce costs and help organizations maximize their region of interest (ROI) (6).

Challenges of AI in healthcare

While AI technology has the prospect of revolutionizing healthcare, its implementation may also give rise to challenges, including concerns related to data privacy, ethical issues, bias of algorithms, and the impact on healthcare professionals (9).

Data privacy is a leading concern in healthcare with AI integration. The emerging use of remote technology in healthcare, including telehealth and telemedicine, threatens its users with a substantial increase in cybersecurity risks (10). The increasing vulnerability of cybersecurity breaches puts patient’s safety, privacy, and financial stability at risk. “From 2014 to 2022, 14,655 data breaches were reported in the United States, out of which the healthcare industry faced 4,959 breaches, the most out of any other sector” (10).

AI systems are due for examination of ethical soundness with issues like transparency, fairness, and accountability (5). Healthcare providers must carefully evaluate AI solutions and ensure they align with ethical guidelines to prevent potential harm and maintain patient trust (5). This is to avoid the bias of exacerbating “socioeconomic class, color, ethnicity, religion, gender, disability, and sexual orientation” (11). Government regulation is needed to ensure safe, effective, and quality AI implementation in healthcare delivery. Development of standards for AI technology is a complete necessity for the medical community. It must revisit current regulatory systems that ensure healthcare AI is responsible, evidence-based, bias-free, and designed and deployed to promote equity (11).

A factor to consider for healthcare organizations is the many changes in the workplace that are required to take place for the smooth integration of AI into the workflow. AI incorporated into the daily operation of healthcare services will dramatically alter workflow processes. Negative impacts on healthcare employees are their jobs and identities being threatened by the presence of AI in the workplace (12). Applying AI technologies will potentially restructure workflow priorities and duties of the healthcare professional.

Real-world examples of AI implementation in healthcare

AI is transforming healthcare. For example, AI applications in medical imaging, specifically in cardiovascular imaging studies, and also physiologic data monitoring with AI, electrocardiograms (EKGs), and electroencephalograms (EEGs), are now being used to detect heart arrhythmia in patients (13). AI is also used for surgical procedures. By harnessing the precision and adaptability of robotic systems, surgeons can navigate delicate anatomical structures with unparalleled accuracy, thereby minimizing trauma to surrounding tissues and reducing post-operative complications (14). The minimally invasive nature of robotic procedures holds the potential to shorten hospital stays, expedite recovery times, and curtail healthcare expenses, ultimately enhancing patient outcomes and experiences (14).

Johns Hopkins University Hospital used predictive analytics with AI technologies in collaboration with GE healthcare partners to efficiently support operational flow (15). As a result of AI technology, assigning a bet to emergency room patients became 30% faster, operating room transfer delays have been reduced by 70%; ambulance dispatch is 63 minutes quicker at picking up patients from other hospitals, and accepting patients with complex medical conditions from different regional and national hospitals has improved by 60% (15).

A premier healthcare organization in the US, the Mayo Clinic, is recognized for its innovation in patient care and health technology. The Mayo Clinic employed AI for cervical cancer screening, which uses an algorithm using “over 60,000 cervical images from the National Cancer Institute to identify” pre-cancerous changes in a woman’s cervix, functioning at a much higher accuracy rate of 91% compared to a trained human expert of 69% accuracy (15).

Future implications associated with AI integration in healthcare

Whether AI could ultimately replace many clinicians is debated, though it is far more likely that the newly emerging technology will enhance clinical productivity (16). One way to make primary care more conducive to AI is by integrating the emerging technology of new-generation AI-powered electronic health records (EHR) into clinical practice. For successful integration of AI in healthcare, the approach of governmental certification of EHRs needs innovative enhancement. The current generation of EHRs is fundamentally flawed and digitally overloaded and needs a significant overhaul. The approach of governmental certification of EHRs is hindering rather than enhancing innovation (16).

There are implications of how AI will impact the healthcare workforce and concerns about developing an ethical framework to regulate its implementation. The greatest challenge of AI adoption in healthcare will be its integration into daily clinical practice. By the next decade, we should see more AI presence in healthcare.

Conclusions

In conclusion, AI integration in healthcare has its opportunities and challenges. Its opportunities are improved diagnosis and treatment, ease of administrative burden, personalized patient care, and cost reduction. Its challenges are concerns related to data privacy, ethical issues, bias of algorithms, and the impact on healthcare professionals. Integration in healthcare fundamentally changes the industry with productive benefits, and its potential for success outweighs its negative consequences. With new emerging technologies, the presence of AI in healthcare is not unnoticed. With more initiative to develop regulations and policies regarding the implementation of AI in healthcare, its adoption will be more attainable for healthcare organizations, and its functional application will become evident in patient-centered care.

Acknowledgments

None.

Footnote

Provenance and Peer Review: This article was a standard submission to the journal. The article has undergone external peer review.

Peer Review File: Available at https://jmai.amegroups.com/article/view/10.21037/jmai-24-232/prf

Funding: None.

Conflicts of Interest: The author has completed the ICMJE uniform disclosure form (available at https://jmai.amegroups.com/article/view/10.21037/jmai-24-232/coif). The author has no conflicts of interest to declare.

Ethical Statement: The author is accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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