Medical – Legal Barriers to Adoption of AI in Healthcare I
One key barrier in AI adoption in healthcare is med-legal implications of using AI algorithms that make predictions and provide recommendations. Clinicians could rely on these to make key decisions about patient management. What if there are issues with those recommendations? What if the patient is harmed as a result of the AI recommendations? who’s […]
Provider Workflow Issues in Adoption of AI – II
One of the key issues with implementing AI solutions in healthcare settings is the fact that these algorithms are often in the cloud and the medical center data would need to leave the institution to be examined using the algorithm. Security and privacy of patient data that may be put at risk because of the […]
Provider Workflow Issues in Adoption of AI – I
One of the key issues with the use of digital technologies in healthcare has been that they need a reliable feed of data to perform as expected and their output needs to be timely so that the clinicians can benefit at the right time. If not, their long-term adoption would be in serious doubt since […]
How Far is Reimbursement for Algorithms in Healthcare? II
Health systems are heavily dependent on procedures and thus will incentivize physicians to continue using them as first-line for treating patients. This can include investments in technologies such as AI to assist in performing colonoscopies. Increased physician satisfaction and productivity will be a major driver of the use cases even without reimbursement. Optimal performance […]
How Far is Reimbursement for Algorithms in Healthcare? I
Reimbursement is critical for any new technology in healthcare. If the use of a new diagnostic or therapeutic technology is not paid for by insurance, there is very little chance that it will gain widespread adoption. Medical innovation is expensive and beyond the reach of most patients or even medical centers if it is not […]
Regulatory Landscape of AI in Healthcare II
The debate over how the FDA should regulate the emerging solutions that use AI for healthcare applications is one that is attracting opposite but truly equally valid points of view. Baku Patel, former head of the FDA Digital Health unit, laid out the rationale for the lighter touch approach by the FDA. He indicated […]
Regulatory Landscape of AI in Healthcare I
Before any new diagnostic or therapeutic, or technology can be used in the practice of healthcare, it has to be deemed safe and effective by certain regulatory bodies. US, EU, Japan, and others have robust requirements for such approvals. This has been the case historically for drugs and medical devices. However, there is a question […]
Mitigating Risk of AI Algorithm Deployment
Evidence-based AI is not exclusive to showing that AI algorithms will improve patient outcomes, improve clinical workflows, or lower the cost of care. Currently there is a great deal of variability in risk-mitigating AI development and deployment practices. Current or continuously emerging evidence and experience with AI development or deployment will allow for mitigation of […]
What Evidence Will Cut it for AI Solutions in Healthcare?
My discussions with many of the experts in AI in Healthcare has highlighted the fact that well-designed, large-scale, multi-center trials have not been done so far. These types of trials would establish the efficacy and safety of these algorithms in the real-world settings where there are different types of patients. Also, the algorithm gets tested […]
Myriad of Issues Facing Adoption of AI in Healthcare
According to a survey of over 12,000 participants conducted by the consultancy PwC, lack of trust and a need for the human element were the biggest hurdles to using AI in healthcare. Another survey by KPMG in 2020 revealed a number of areas of concern for healthcare executives in regards to AI . One is in the […]