Explore Your Local Site

Looks like you've landed on our   site. Let's take you home:    

Please note that the content and products on the    site might not be available in your region.


Continue on the current website:  


How to Assess AI Applications and Their Potential Reimbursement


AI applications have the potential to lift current burdens in radiology. But the willingness to pay for an application depends on the main benefiter.


Speaker: Franziska Lobig, Bayer Pharmaceuticals, Digital Solutions


Reimbursement Report


Currently, there are nearly 300 FDA-approved and CE-marked AI algorithms on the market – but just eight of them get reimbursement in the US. Franziska Lobig, Bayer Pharmaceuticals, Digital Solutions, discussed the main parameters on how to assess the applications’ added value. Added value is an indispensable condition for future reimbursement.


Baseline: Heavy Workload, Diagnostic Errors, Malpractice Lawsuits

The increasing number of medical images and the immense workload for radiology staff seriously impact patients, radiologists, and hospitals:

  • Missed diagnosis and diagnostic errors lead to inappropriate treatment and delayed patient care with potentially drastic consequences. 40,000 – 80,000 deaths per year in US due to of diagnostic errors have been estimated according to the whitepaper “The Human Cost and Financial Impact of Misdiagnosis” by pinnaclecare.com.4
  • By 2030, it is forecasted that there would be a workforce undersupply of radiologists and radiation oncologists of 25 per cent and 63 per cent respectively.2
  • With estimates of average diagnostic error rates ranging from 3% to 5%, there are approximately 40 million diagnostic errors involving imaging annually worldwide (Itri et al. 2018).3
  • Approximately 75% of malpractice lawsuits filed against radiologists are related to diagnostic errors (“Radiologist Malpractice Lawsuits and Settlements” by millerandzois.com)5


AI Adds Value – Who Benefits Most?

Value in healthcare can be considered as patient health outcome in relation to the cost of delivering these outcomes. The willingness to pay for an AI application depends on who is the main benefiter.

Benefit can be improved by:

  • Gaining better patient outcome : added value for the payer who wants to provide optimal care for reasonable cost
  • Reducing costs of providing the outcome : added value for the hospital


Clinical Utility for Application Reimbursement

Clinical utility is the evidentiary standard to evaluate solutions for reimbursement. It requires:

  • Fundamental change in treatment – just rising the detection rates does not count
  • Improved outcomes for the patient – change in treatment does not necessarily mean the patient lives longer or has a better quality of life


Both these aspects need to be proven when reimbursement of AI applications is being discussed.


Demonstrating Clinical Utility

Certain attributes of underlying diseases make it more likely to exhibit the clinical utility of an AI application:

  • High clinical burden of an indication – evidently, stroke counts for higher burden than a broken arm
  • Unmet needs in current diagnostics or management – is there a problem that can be solved for the radiologist, such as inconsistent outcomes
  • Ability to impact the clinical decision-making – this can only be addressed when multiple treatment options are available
  • Short duration between imaging procedure, therapy decision, and outcome – you need all these parameters to generate robust clinical evidence
  • Impact on healthcare resource utilization – for example by reducing the number of patients that need further diagnostic tests



1. Mc Donald RJ, Schwartz KM, et al. The effects of changes in utilization and technological advancements of cross-sectional imaging on radiologist workload 2015; 22(9)

2. Availability and Accessibility of Diagnostic Imaging Equipment Around Australia (2018); 4.1(53)

3. Itri JN et al. Fundamentals of Diagnostic Error in Imaging 2018; 38(6)

4. The Human Cost and Financial Impact of Misdiagnosis whitepaper by pinnaclecare.com

5. Radiologist Malpractice Lawsuits and Settlements. Article by Miller & Zois Attorneys.



Presentation Title: Driving AI Innovation to Assess Reimbursable Apps

November 2022

Author: mh/ktg