By Kal PatelMD, MBA, CEO and Co-Founder, BrightInsight
THE Journal of Future Healthcare notes that AI in healthcare “continues to rapidly accelerate, with potential use cases demonstrated in healthcare including drug discovery, virtual clinical consultation, disease diagnosis, prognosis, medication management and health monitoring”. Following the JP Morgan Healthcare (JPM) 2024 conference, it is clear that AI will continue to be a hot topic in 2024.
At JPM, I discussed the impact of generative AI in healthcare with Amar Goelco-founder and CEO of Bito, a revolutionary productivity tool for developers that accelerates software development using AI models, and Dr. Théodore Leng, a world-leading innovator in the field of medical and surgical retina, who works in the Department of Ophthalmology at Stanford University as Director of Clinical and Translational Research and Director of Ophthalmic Diagnostics. Hearing their perspectives helped broaden my own understanding of the opportunities AI offers.
AI as a mindset
Many in the healthcare industry have been cautious about adopting the latest digital innovations and AI takes this concern to the next level. AI comes with a learning curve and changes the way you work, regardless of the industry. Amar shared an anecdote about how his son, when he encounters a problem in code development, instinctively uses ChatGPT to find and fix the problem instead of thinking about a solution.
Generative AI can read code and test it much faster, what used to take days in minutes. In fact, software developers could be 10 to 20 times more efficient over the next five years. Generative AI is easy to experiment with, although clinical applications require near-perfect consistency and accuracy.
AI programs need to be reliable, repeatable, cost-effective, and fit into a current workflow – this is the hardest part and will require focus and possibly a partner. Some best practices are emerging for selecting the right technology startup partner, including:
- Identify and prioritize your organization’s AI use cases
- Test AI initiatives in areas of clear internal technical expertise and in the required domain
- Work with a partner who is strong in areas where you are not
- Review the potential partner’s management team, customers and flagship programs. Many companies are rebranding to claim their expertise in AI, but how far does it go?
As Dr. Leng pointed out, many AI initiatives at Stanford are led by passionate physicians, who then involve their medical center IT teams before deploying them.
The impact of generative AI on healthcare
About 30% of global data is currently generated by healthcare, offering considerable information when analyzed correctly. This amount is difficult to analyze meaningfully without the help of AI. Improving efficiency will be a key focus of AI in the early days, with the aim being to make patient care more effective, safer and more efficient.
Additionally, patient email volume is now five times what it was a few years ago and Stanford receives more than a million messages per year, placing a significant burden on healthcare teams. . The health system is testing ChatGPT to generate draft responses. Clinicians can use, edit, or delete content, and this resource improves clinician satisfaction. Another potential use leverages AI with ambient voice to help enter notes from a clinical encounter into the EHR.
During drug discovery and development, clinicians must learn to use generative AI, for example using clinical trial data, to guide the decision-making process. Analyzing retinal images in clinical trials to identify biomarkers of disease progression, for example, can give an earlier signal whether a treatment is working.
Bringing a platform approach to AI
At BrightInsight, we work with biopharmaceutical clients to combat multiple disease states. One of our partners, Woebot Health, has built an “empathy engine” that uses its ability to learn to develop a lasting and sympathetic relationship with the patient and can be integrated into applications created with the disease management solution of BrightInsight. Consider patients suffering from needle phobia, which affects virtually all medical procedures and approximately 63.2% of patients, which leads almost half to avoid blood tests and blood donations, and a third to skip vaccinations. Many treatments are now self-infused at home, requiring the person to overcome their fear of needles to receive the medication.
Woebot’s Empathy Engine helps patients explore and overcome their needle phobia by establishing a sympathetic and understanding relationship as a prelude to a therapeutic tool. The BrightInsight platform allows Woebot to introduce conversational AI tools to these patients to improve their quality of life, helping them overcome needle anxiety and helping them engage in therapy and recovery. exit as their condition improves.
A secure platform for compliant digital health solutions, like that of BrightInsight Platform, can provide the framework and capabilities needed to support the deployment and execution of AI initiatives. It is designed to collect data from various sources, including devices, EHR systems, etc., and run these sets through any algorithm. Because it is modular, flexible and scalable, the algorithm can be easily and frequently updated as it learns and changes, allowing it to remain relevant as requirements evolve.
As AI applications continue to grow, the question of job replacement arises. As Dr. Leng explained, while technology won’t replace people, doctors who use AI will replace doctors who don’t use AI – it will be like any other tool, like a stethoscope or a ultrasound, we will have to adapt.
Dr. Kal Patel is CEO and co-founder of BrightInsight, the leading global regulated digital health platform for biopharmaceutical and medical device companies.
The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.
