Our Perspective
As AI algorithms become more sophisticated and their accuracy improves, a shift towards more automated diagnostics is likely, particularly for routine cases.
The average hospital generates about 50 petabytes of data annually. Medical imaging, including X-rays, MRIs, and CT scans, accounts for 80% of that data.
These images are vital for diagnosing and treating numerous conditions. However, as the complexity and quantity of imaging data increase (partly due to a growing emphasis on early diagnosis), so do the challenges faced by radiologists and healthcare professionals. In 2023 alone, approximately 3.6 billion medical imaging diagnostic procedures were performed globally, including around 710 million in the United States. This volume is expected to grow significantly, with projections estimating that global imaging procedures will surpass 5 billion annually by 2030.
Radiologists, often overwhelmed by the sheer volume of imaging studies they must review, can find relief in the assistance artificial intelligence (AI) provides. Their workloads, compounded by the need to use multiple software applications to gather patient data, review images, and generate reports, is lightened by AI's capabilities. The push for higher productivity within healthcare systems and a global shortage of radiologists are not going away, but AI's support can help alleviate these pressures, reducing physician burnout.
Burnout has profound implications. Fatigue can impair a radiologist's ability to concentrate, increasing the risk of diagnostic errors. Even small mistakes can severely affect patient outcomes in a field where precision is paramount. Diagnostic errors — including misdiagnoses, missed diagnoses, and delayed diagnoses — occur in approximately 10-26% of all cases. These errors are a leading cause of medical malpractice lawsuits against radiologists, accounting for about 75% of such cases, further emphasizing the critical need for accuracy in medical imaging.
Picture archiving and communication systems (PACS) enable radiology teams to manage the growing volumes of medical images they generate daily. However, the time-consuming nature of current PACS workflows can lead to delays in diagnosis and treatment, further impacting patient care. Providers can turn to AI to make the most of PACS, making medical imaging more efficient, precise, and, ultimately, more patient-centered.
How AI Is Enhancing PACS for Better Outcomes
AI offers solutions to many challenges inherent in current PACS systems, such as managing the ever-increasing volume of medical imaging data, detecting subtle patterns that may be overlooked in manual reviews, reducing the time required for radiologists to interpret complex cases, and minimizing the occurrence of diagnostic errors. Below are some of the key ways in which AI is poised to enhance PACS:
How AI Is Integrated into PACS
To effectively integrate AI into a PACS system, business leaders should partner with AI technology providers specializing in healthcare imaging. The process typically involves either integrating AI through APIs that allow the PACS to communicate with external AI services or adopting an enterprise-grade AI-for-PACS platform seamlessly connected to or integrated with the existing PACS infrastructure. This decision should be based on the organization's specific needs, the compatibility of the AI solution with the current PACS, and the desired level of customization. It's also crucial to talk with potential vendors to understand their solutions' technical requirements, support, and scalability to ensure a successful enhancement of the PACS system with AI capabilities.
Addressing Challenges: Trust, Validation, and Data Privacy
Despite the clear benefits, integrating AI into PACS has its challenges. One of the most significant hurdles is building trust within the medical community. Radiologists and other healthcare professionals must be confident in AI's ability to accurately and reliably assist in diagnostics. This trust is built through rigorous validation processes, where AI algorithms are tested against large datasets, and their performance is benchmarked against human experts.
Regulatory compliance is another critical factor. In the United States, for instance, the Food and Drug Administration (FDA) classifies PACS as a medical device, meaning that any AI algorithms integrated into these systems must undergo stringent validation and approval processes. Ensuring that these algorithms meet the high standards required by regulatory bodies is essential for their adoption in clinical practice.
Data privacy is also a significant concern, especially given the sensitive nature of medical imaging data. AI models require access to large datasets to improve their accuracy, but this data must be carefully handled to protect patient privacy. Anonymization techniques are essential to ensure that patient identities are not compromised during the training and validation of AI models. There is ongoing work to develop systems that allow for the sharing of anonymized imaging data on a large scale, enabling further advancements in AI while maintaining strict privacy standards.
The Future of Medical Imaging: Innovations and Growth Opportunities
Looking ahead, integrating AI into PACS is likely to drive significant innovations in medical imaging. As AI algorithms become more sophisticated and their accuracy improves, a shift towards more automated diagnostics is expected, particularly for routine cases.
The future of medical imaging is also likely to see greater integration of AI across different imaging modalities and specialties. While radiology and cardiology are the primary users of PACS today, other fields, such as dermatology, neurology, and pathology, are beginning to adopt imaging technologies that could benefit from AI enhancements.
Integrating AI into PACS represents a significant leap forward in medical imaging. By addressing physician burnout, workflow inefficiencies, and diagnostic errors, AI-enhanced PACS systems can improve patient care and outcomes. As these technologies continue to evolve, the future of medical imaging looks brighter than ever.
About the Authors
Hans U. Poulsen
Domain & Consulting Leader, Europe
Hans has over 25 years of consulting experience serving life sciences and medical device companies across the US, Europe and Japan. As Partner and Domain and Consulting leader in Wipro’s Life Sciences and Medical Devices practice, he focuses on driving digital technology-enabled innovation and implementation in the R&D and regulatory value chains.
Hans is a recognized thought leader in growth and innovation strategy for R&D organisations and his work on the “Return on R&D’ has been covered by the Financial Times, Reuters, the Nikkei Investor Daily, and presented at the industry conferences including DIA, Citibank Annual Investor conference and Wharton Business School.
Lance Manley
Senior Domain Consultant and PACS Architect
Lance has over 20 years of experience in the medical imaging IT world with a focus on system architecture, integration and implementation. He has worked for large healthcare organizations, private practice, vendors and medical device manufacturers. He is passionate about bringing creative workflows to life and delivering innovative systems to the medical providers.
Gaurica Chacko
Vice President and Global Life Sciences Industry Leader
Gaurica is a proven industry leader with more than 20 years of experience in management consulting and the health sector, with a focus on business and digital transformation, cloud development, health equity, and product strategy. Gaurica works at the intersection of business and technology and specializes in connecting with clients and partners to find innovative solutions to complex business problems. Gaurica has been a key contributor in the areas of digital inclusion, health-tech innovation, and public-private partnerships, and serves as an executive member of the World Economic Forum-Edison Alliance. She has been listed as one of the Top 50 Innovators in AI for 2023 by Intelligent Health AI. Gaurica has a passion for bringing innovative therapies and diagnostics to underserved patient populations and is driving global cross-stakeholder initiatives to leverage AI for social good.
