Industry Perspective: ROI for AI & Standards in AI

Summary of AI/ML Interviews with Industry by the SIIM Machine Learning Industry Liaison Subcommittee

The ML Industry Liaison Subcommittee of the SIIM Machine Learning Committee completed 35 interviews with the intent of collecting insights into the vendor’s perceptions of Artificial Intelligence/Intelligent Automation and SIIM’s role in this technology space.

The team effort allowed the interviews to span across vendor segments of technology companies, imaging/iron vendors, PACS vendors, and industry veterans. The interviews produced qualitative responses that are summarized by the team below.

The survey included companies/people interviewed who were not members of SIIM directly and some were not corporate sponsors as well SIIM members/sponsors.

In the context of the interviews, we did not define the terms of AI, ROI nor standards for the respondents so that they were free to elaborate on the topics. Below are the findings by topic, summarized as themes, with key quotes where the team wanted to call out illustrations

Michael LaChance and Mitchell Goldburgh as co-chairs of the Industry Liaison Subcommittee wanted to personally thank all of the company representatives and our committee members for their participation in this exploration of AI/ML adoption.

Is there an ROI as an industry – in the context of billions of dollars invested in AI software development thus far

• Without provider reimbursement, direct financial ROI for disease detection algorithms – and particularly so for algorithms targeting a single disease – is generally not perceived to sound business reasoning for vendors or providers. Further, although several algorithms have been granted reimbursement, broad reimbursement is not anticipated.
• Likewise, direct financial ROI from productivity tools (including triage) is not perceived as a sound business rationale for vendors or providers with the possible exception of large health systems and large reading practices.
• Providers can realize ROI from the advancements in precision that AI offers in analysis and downstream decisions, therefore, impacting the cost of care pathwaysRespondent Quote: ‘The greatest ROI potential for diagnostic radiology information is outside of the department. It is with AI applied to diagnostic radiology information specifically targeting care pathways, outcomes, population health’
• Multiple mentions indicated existing reimbursement models assume an acceptable performance and continuous improvement so additional reimbursement for AI won’t be for improvement in triage, detection, diagnostic efficiency.Respondent Quote: ‘It’s about answering two questions: ‘Am I going to die today?’ (better diagnosis and outcomes) and ‘Do you know how I am going to die (in the future)? (better population health/wellness)’
• Multiple versions of responses indicated that AI can and will deliver clinical and operational benefits. Further, many believe adoption is a must-have to achieve clinical and operational outcomes, but there won’t be direct new revenue and/or increased margin hence direct financial ROI to justify new investment.