Dr. Ron Razmi discusses his book, AI Doctor: The Rise of Artificial Intelligence in Healthcare, which focuses on the applications of AI in various areas of healthcare. Ron talks about AI as a foundational technology that uses statistical methods to analyze data and understand its meaning. It can be applied to various areas, such as coding and documentation, and can create algorithms to analyze heartbeats and detect abnormal rhythms.

AI Use in Diagnostics

In diagnostics, AI can be helpful in analyzing clean structured data, such as radiology files from CAT scans or MRIs. AI can be trained to identify bleeding or abnormalities on these scans, but it must be trained on specific abnormalities. Currently, AI excels in radiology due to the digitized and structured data, but it's not as effective in narrative formats. Ron states that AI's applications in healthcare are vast and deep, and while it's still in its early stages, it has the potential to revolutionize various sectors, including healthcare.

AI Tools in Radiology and Triage

AI is increasingly being used in various fields, including radiology, dermatology, and sound AI. In radiology, AI tools are helping radiologists identify potential issues that might have been missed in traditional workflows. There are applications that read CAT scans for bleeding and stroke in acute settings, allowing radiologists to quickly identify and treat stroke patients. This helps in regaining function and ensuring patient recovery. AI can also aid in triage, prioritizing tasks based on urgency.

AI in Dermatology

In dermatology, AI applications can help diagnose skin lesions. Additionally, sonar technology can be used to monitor people's activity and detect falls. This passive data collection method allows AI to analyze the signals and take action, reducing the need for manual data entry and manual data input. This technology is particularly effective for elderly individuals who may not want to be monitored with cameras, as 50% of falls occur in the bathroom. This technology is particularly useful for those who prefer to stay home but still need constant monitoring, such as those in assisted or independent living facilities.

AI in Cardiology

In cardiology, AI tools can help read EKGs, which are crucial for diagnosing heart conditions. Historically, algorithms have been used to read EKGs, but they were often basic and inaccurate. AI has shown great promise in finding abnormalities on single lead data collection, as it can discern the rhythm of an EKG from a live core or smartphone application. This field-based data collection could significantly reduce the simple analytic and downstream work needed by clinicians in the medical staff.

AI in Therapeutics

AI takes action in therapeutics, such as providing assistance to patients with mental health issues. AI chatbots can interact with patients who need help and provide frontline assistance until they see a mental health professional. Generative AI has improved natural language processing capabilities, which has been a problem area for AI in healthcare due to the heavy use of medical jargon in doctor's notes. This will allow for more efficient interactions with healthcare consumers and better guidance in their care. However, there is still much work to be done in this promising area.

Technology and Medication Adherence in Healthcare

The conversation turns to medication adherence, which is a significant problem in healthcare. Long-term studies show that people who have had a heart attack are more likely to stay compliant with their medications, with the refill rate for statins being the highest documented rate. However, most people do not follow their prescription advice. Technology is part of the solution to this problem. AI technology can potentially analyze data and interact with patients at the appropriate moment to ensure they are aware of the needs, issues, and dates of medication use or the lack of, which is crucial in healthcare.

How AI Helps Doctors

AI applications in healthcare can help alleviate the burden of documentation work for physicians and nurses. One example is the use of AI in critical care settings, where doctors often spend time typing notes into the electronic health record, which can lead to missed information and negatively impact the quality of care. AI can also analyze conversations using natural language processing, which can identify the meaning of words and improve communication. For example, AI can listen to conversations and extract key elements that need to be documented, allowing doctors to focus on the patient and generate notes in their preferred format. AI can also perform downstream tasks, such as prescribing medication, making referrals, and creating prior authorization letters. This could save time for medical staff, preventing them from seeing more patients and making them less productive. Another use case could be for AI to assist nurses in creating notes for patients based on interaction with them, reducing the time spent documenting. This could make a significant difference in the quality of care and well-being since it allows medical professionals to focus on the patient instead of taking notes.

Evaluating AI Healthcare Applications

Ron’s book goes beyond cheerleading and emphasizes the business and clinical barriers to adoption. He talks about the importance of evaluating the business model of AI products or applications, considering the incentives of buyers and the potential for job loss or revenue reduction. As an investor, he suggests considering the pain points that AI could address, such as staff shortages and burnout, and how AI could help medical centers and pharmaceutical companies improve their clinical trials. Ron also discusses the importance of understanding the value proposition of AI products in medical settings. He shares his experience as an investor in the healthcare AI space, advising companies and funds on identifying great use cases and evaluating barriers. Ron recommends reading sources like healthcare AI digests, health tech news, and interviews with experts to stay informed about trends and the latest AI applications.

Timestamps:

04:00 AI applications in radiology and dermatology, including AI-assisted diagnosis and triage

10:54 Using AI to analyze passive data collection from sonar/radar in indoor environments for health monitoring, including fall detection and heart rate measurement

15:12 AI in healthcare, particularly in diagnostics and therapeutics

21:22 Using AI technology to improve medication adherence

26:40 AI applications in healthcare, including documentation assistance and quality of care

34:41 AI in healthcare, investment considerations, and product evaluation

39:44 AI in healthcare with a former doctor turned investor

Links:

LinkedIn: https://www.linkedin.com/in/ronald-m-razmi-md-2b55b8/

The Book: AI Doctor: The Rise of Artificial Intelligence in Healthcare

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573. Ron Razmi, AI Doctor: The Rise of Artificial Intelligence in Healthcare

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