This HealthAI Investor Chat focuses on the discussion of artificial intelligence in healthcare applications and explores the changes the AI technologies can bring to the service of patients.
Post by Aleksey Krylov
I attended select HealthAI events in recent days. DigitalHealth InvestorTalk: Get Generative AI To Work For You with Steven Wardell was memorable. This was by no means an exhaustive analysis of the HealthAI opportunity, but a few observations stuck in my mind that are worth highlighting. These are in no particular order:
The reason we are talking about AI is attributed to the speed with which it has become a part of our lives. ChatGPT acquired 1 million users just 5 days after launching in November 2022. This compares to Instagram’s 2.5 months and Netflix’s 3.5 years. These may not be comparable: Different times, different technologies, and different adoption barriers. But one can’t argue with the speed of the user acquisition that ChatGPT enjoyed.
The explosive use of AI, ChatGPT specifically, is attributed to the release of the friendly user interface that enabled regular Joe to start interacting with the AI engine.
AI (artificial intelligence) has been around for years, with some technologies dating back to 1950s, according to this Harvard’s History of Artificial Intelligence.
Driven by processing speeds, AI is great at working with big data. Classification of data is one of the tasks that it is great to work through.
The examples used in Steven Wardell’s show involved analyzing various HIV patient behavioral data to assess the likelihood of drug regiment compliance failure. In that example, the nurses were given a list of patients who were at risk, and nurses called those patients to reinforce the importance of compliance.
Another example discussed was data processing with the goal of new drug discovery. AI would analyze biological or DNA data to identify new promising drug targets.
Some of these AI applications are available only because of the existence of proprietary data that can enable the analysis. Without the data, the AI is ineffective.
· Other applications may involve analysis of the clinical data and/or documentation to identify patterns around certain diseases.
· Yet another example may involve analysis of the images (e.g., X-ray or MRI) that supports doctors in interpreting the results.
AI working in conjunction with human-physician is the most promising combination of th AI applications in the short term. AI solutions can give ideas to doctors; they can increase productivity or help prevent mistakes.
One discussion topic touched upon is the reliance on AI… At what point, we humans may rely on technology for research, independent thinking and/or verification of the data used in the analysis? Are we going to believe the AI recommendations mindlessly? Or will we critically assess the suggested recommendations?
Although I reserve the right to change my opinion, I am leaning toward the conclusion that AI is here to stay. It has been around for some time, and some of these applications are already widely used (we started talking about them a lot in the last several months). Here are select applications where big-data crunching can be valuable to physicians and patients alike:
Research and Drug Discovery: We talked about how AI can accelerate the drug discovery process by analyzing vast amounts of biomedical data, predicting drug efficacy, and identifying potential targets for therapy. AI algorithms can also assist in clinical trial design, patient recruitment, and monitoring drug safety.
Improved Diagnostics and Decision-Making: We also discussed AI helping interpret medical images, such as X-rays and MRIs, helping doctors make more informed decisions and potentially improving patient outcomes.
Personalized Medicine: AI can enable the analysis of patient data, including medical records, genetic information, and lifestyle factors. This information can be leveraged to develop personalized treatment plans, predict disease risks, and identify optimal therapies tailored to individual patients.
Streamline Healthcare Operations: This is a big one for improving healthcare costs. AI technologies streamline administrative tasks, automate routine processes, and improve operational efficiency in healthcare organizations. This includes tasks like patient scheduling, medical coding, and billing, freeing up healthcare professionals to focus more on patient care. Value add will come from quality control the technology can help enforce: Are charts complete? Are appointments scheduled? Are follow-ups lined up?
Disease Prevention and Early Detection: AI algorithms can identify patterns and detect subtle changes in patient data, aiding in early disease detection and prevention. By analyzing diverse data sources, AI systems can identify risk factors and provide timely interventions, potentially reducing the burden of chronic diseases. It may involve solving access to data and enabling patient data to stream into the AI engine in real-time to increase the efficiency of this solution.
Remote and Telemedicine: AI technologies facilitate remote patient monitoring, telehealth consultations, and virtual care. These solutions can improve access to healthcare, especially for underserved populations and those in remote areas, and enable continuous monitoring of patients with chronic conditions.
Given these factors, AI in healthcare can potentially be transformative in the long term. For the benefit of the patients, it behooves the industry to continue adopting and enhancing AI technologies with proper safeguards in place.
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