‘THEY’RE COMING’: Doctor explains how AI scanning is rolling out in medical field

AI Adoption & Transformation in Healthcare

Key Concepts:

• AI in Healthcare: Application of artificial intelligence technologies to improve various aspects of healthcare delivery, from administrative tasks to diagnostics and treatment.
• Electronic Health Records (EHRs): Digital versions of a patient’s paper chart, often cited as a source of administrative burden for physicians.
• Administrative Burden: Excessive paperwork, coding, and insurance pre-authorization processes in healthcare.
• AI-Powered Note Taking: Technologies utilizing speech recognition and natural language processing to automatically draft clinical notes during patient encounters.
• Radiomics/AI-Assisted Imaging: Utilizing AI to analyze medical images (MRI, CAT scans) to detect anomalies and assist radiologists.
• Digital Therapeutics: Utilizing technology, including AI, to deliver therapeutic interventions directly to patients (e.g., Hinge Health).
• Insurance Reimbursement: The process by which healthcare providers receive payment for services from insurance companies, a key barrier to AI adoption.
• Outcomes Data: Evidence demonstrating the positive impact of a technology on patient health outcomes, crucial for insurance coverage.

I. Current State of AI Adoption in Healthcare

As of September 2025, 22% of healthcare organizations have deployed commercial AI solutions. This adoption rate is more than double that of the broader US economy, according to Menlo Ventures. This rapid uptake is driven by significant frustrations within the healthcare system, including long wait times, excessive paperwork, and a perceived lack of engagement from physicians due to the demands of Electronic Health Records (EHRs). Patients also face high costs and unexpected billing practices.

II. The Administrative Burden & AI Solutions

A major driver for AI adoption is the overwhelming administrative burden in healthcare. Over the last few decades, the number of healthcare administrators has increased by 3,000%, while the number of doctors has increased by less than 200%. This imbalance hasn’t necessarily translated to improved quality of care. AI is being leveraged to streamline these administrative processes, with a focus on reducing the workload for medical professionals.

Specific examples include:

• AI-Powered Note Taking (Ambiance, Abridge): Technologies that listen to doctor-patient conversations and automatically generate clinical notes. These are being implemented at institutions like the Mayo Clinic, Cleveland Clinic, and Kaiser Permanente. Early reports suggest these tools are significantly improving physician satisfaction and potentially preventing burnout. One physician reported being able to continue practicing medicine for another 30 years due to the time saved.
• Automated Insurance Pre-Authorization: AI is being developed to automate the process of obtaining insurance approval for procedures, reducing delays and administrative overhead.

III. AI in Diagnostics & Imaging

AI is demonstrating potential in enhancing diagnostic capabilities, particularly in medical imaging. AI algorithms can identify subtle anomalies in MRIs and CAT scans that might be missed by the human eye. However, widespread adoption of these technologies is contingent on insurance companies providing reimbursement for their use. The need for robust, real-world studies demonstrating improved accuracy and patient outcomes is a key requirement for securing insurance coverage.

IV. Emerging Technologies & Companies

Several companies are at the forefront of AI innovation in healthcare:

• Hinge Health: A digital therapeutics company utilizing smartphone cameras and AI to monitor physical therapy movements at home, providing real-time feedback to patients. This addresses a shortage of physical therapists and allows for more convenient and accessible care. Hinge Health recently had a successful IPO.
• HeartFlow: Utilizes AI to analyze CAT scans of the heart to assess the risk of heart disease. Also recently completed an IPO and is performing well in the public market.
• Sward Health: Similar to Hinge Health, focusing on physical therapy and expanding into chronic disease management. Considered a potential IPO candidate for 2026.
• Coher: Streamlines the process of obtaining authorization for medical procedures, reducing administrative delays.

V. Barriers to AI Adoption & Insurance Reimbursement

The primary barrier to wider AI adoption in healthcare is the requirement for demonstrable improvements in patient outcomes. Insurance companies require evidence from real-world studies proving that AI technologies enhance the accuracy of diagnoses, improve surgical outcomes, or otherwise positively impact patient health.

Dr. Ron Rasmi notes that Medicare is beginning to assign codes for certain AI technologies, signaling a shift towards greater acceptance and reimbursement. However, progress is expected to be faster on the administrative side, where insurance reimbursement is not required, as these solutions immediately reduce costs and workload.

VI. The Impact on the Doctor-Patient Relationship

While there is initial apprehension among medical professionals regarding new technologies (stemming from past experiences with EHRs), AI-powered tools like automated note-taking are showing promise in freeing up physicians’ time. This allows for more focused patient engagement, reduced burnout, and potentially improved quality of care. The expectation is that AI will augment, not replace, the role of the physician.

Notable Quote:

“Medical professionals are usually traumatized when they hear there's a new technology coming because the last time they were given a new piece of technology, electronic health records, it ended up creating a lot more work than saving work for them.” – Dr. Ron Rasmi.

VII. Regulatory Changes & Potential Impact

The Trump administration is considering rolling back parts of the Biden administration’s health information technology playbook, potentially reducing transparency and removing requirements for standardized healthcare reporting protocols. The stated goal is to reduce “onerous red tape” and lower costs for developers. The potential impact on AI adoption and patient access remains to be seen.

Conclusion:

AI is rapidly transforming the healthcare landscape, driven by the need to address systemic frustrations and inefficiencies. While challenges remain, particularly regarding insurance reimbursement and the need for robust outcomes data, the early successes of AI-powered solutions in areas like administrative tasks, diagnostics, and digital therapeutics suggest a promising future for the technology’s role in improving healthcare delivery and patient experience. The focus is shifting from simply implementing AI to demonstrating its tangible benefits and integrating it seamlessly into clinical workflows.