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Andy Pollen

Clinical AI: Improving Diagnosis and Workflow

aiOS, C-Suite, Information Technology, System Integration
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Clinical AI vs. Healthcare AI

The healthcare industry is full of words and terms, often used interchangeably, but with subtle differences:

  • There’s the frequently misused healthcare (the industry at large) versus health care (the act of delivering care)
  • The more colloquial doctor (used by many professions, not just medicine) versus physician (someone with a medical doctorate)
  • Even BAND-AID® (the name brand of a small adhesive bandage) versus bandage (a variety of materials used to cover and stabilize wounds and injuries)

Recently another set of false synonyms is on the rise: clinical artificial intelligence versus healthcare artificial intelligence. The easiest way to differentiate between the two is to use the example of healthcare (macro) versus health care (micro) noted above.

What is Clinical AI?

Clinical AI is the segment of healthcare AI that specifically focuses on AI technologies used to improve patient outcomes. It incorporates different types of AI in healthcare (i.e. machine learning, deep learnin, natural language processing, computer vision) to help enable better, faster and more accurate diagnostic and treatment decisions.

What is Healthcare AI?

Healthcare AI is an umbrella term that encompasses the many different forms of artificial intelligence used to support various health care functions. These could be administrative, financial, operational or clinical – from automated scheduling to virtual care assistants and supply chain management tools.

Segments of Healthcare AI

Infographic titled 'Segments of Healthcare AI' highlighting various applications of AI in healthcare. The segments include Administrative, Financial, Operational, and Clinical. Each segment lists specific functions. Administrative: Automating manual and repetitive tasks like registration forms, scheduling, and customer service. Financial: Improving financial and accounting practices including billing & claims, insurance eligibility, and revenue cycle management. Operational: Creating smart, integrated operation models involving supply chain, facilities, and process optimization. Clinical: Enabling better diagnostic and treatment decisions through diagnostics, monitoring, and care delivery.

While that’s a highly simplified definition of clinical AI and healthcare AI, it’s worth noting there is still nuance and overlap between the two.

For instance, at Aidoc, a clinical AI company, our focus is intelligent AI that can help individuals process large amounts of data, discover trends and accelerate decision making. This not only benefits clinicians and patients but also the health system at large with positive downstream implications on resources, revenue and costs. While our solutions can help reduce administrative burden (common examples of healthcare AI), it all stems from coordinated and efficient patient care (clinical AI).

AI for Clinical Workflows

Clinical workflows are complex across the board, but to highlight the value of AI in clinical workflows, let’s focus on aortic aneurysms (AA). This condition requires precise, timely diagnosis and follow-up care. AI has transformed how these workflows function, streamlining the process form diagnosis to treatment. Take the example of Yale New-Haven Health, where AI significantly enhanced the management of abdominal aortic aneurysm (AAA) patients. Traditionally, the pathway for AAA diagnosis and care was slow and fragmented, often resulting in mossed follow-ups and delayed interventions. With AI, the process is streamlined– automated alerts ensure that critical findings are promptly flagged, helping clinicians intervene in a more timely manner on relevant cases. AI integrates seamlessly into existing systems, enabling rea-time evaluations and ensuring patients are on the correct treatment paths without delays. These advancements not only improve patient outcomes but optimize clinical efficiency, making AI an indispensable part of modern healthcare workflows.

To learn more about Yale New Haven’s AAA workflows, view our full webinar, “Transforming Aortic Aneurysm Patient Care” here.

How Clinical AI Can Help Reduce Health System Costs and Increase Efficiencies

Clinical AI doesn’t only have the potential to enhance patient care; it can also act as a tool for cost reduction and increased efficiency across health systems. By connecting disparate devices and platforms, AI eliminates bottlenecks in workflows, improving operational efficiency. For example, AI could lead to significant gains in radiologist read times and reduced emergency department lengths of stay — key factors in lowering overall healthcare costs. These efficiencies are not isolated; they ripple across the system. The integration of AI as a clinical partner can empower health systems with optimized resources and streamlined care delivering, benefiting both patients and bottom lines.

Want to explore more on clinical AI? Visit our healthcare AI education and resource hub.

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Andy Pollen

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