AI in Consumer Healthcare: Separating the Wheat from the Chaff

In an era where healthcare is increasingly digital and personalized, Artificial Intelligence (AI) offers a host of promising solutions. From digital therapeutics and pharmaceutical R&D to telehealth services, AI has the potential to revolutionize consumer healthcare. However, as with any buzzword, it's crucial to distinguish between genuine innovation and mere hype. This blog aims to navigate the evolving landscape of AI in healthcare and offers some critical questions to discern the real deal from the impostors.

The Promising Sectors

1. Digital Therapeutics

AI algorithms can analyze data from wearable devices to recommend lifestyle changes, monitor vital stats, and even predict flare-ups of chronic conditions.

2. Pharma Fulfilment

Machine learning can optimize supply chains, reducing costs and increasing efficiency, thus ensuring that medications reach consumers faster and at a lower price.

3. Pharma R&D

AI can drastically speed up the drug discovery process, identifying potential drug candidates in a fraction of the time it traditionally takes.

4. Telehealth

Through AI, virtual consultations become smarter. Predictive analytics can flag issues before they become severe, and automated follow-ups ensure better long-term care.

5. Consumer Accessibility

AI can customize health information for individual consumers, making it more straightforward and more actionable.

The "AI First" Pretense Among Startups

Startups frequently claim to be "AI First" companies, heralding a new age of innovation. While this may be true for some, it's often an embellished narrative for many others. Writing a simple API call to existing AI solutions like ChatGPT does not make a company AI-centric, especially when the data points available are minimal.

Questions to Ask a Startup

If you're interacting with a startup that claims to be revolutionizing healthcare through AI, consider asking the following questions:

1. What specific problem is your AI solution solving?
2. How did you train your AI algorithms, and what data sets were used?
3. How do you ensure data privacy and compliance with healthcare regulations?
4. What quantifiable outcomes have your AI solutions achieved?
5. How does your solution improve upon existing technologies or methods?
6. Do you have any peer-reviewed studies or third-party validations to support your claims?

Medlify's AI-driven Approach

At Medlify, we're pioneering the effective use of AI in healthcare by ingesting analytics data from our platforms and user behavior, as well as wearable device data. The data is first standardized to FHIR (Fast Healthcare Interoperability Resources), a crucial step for ensuring interoperability and high-quality analytics. This standardized data is then ingested into a healthcare-specific data lake. Our R&D teams collaborate with data scientists to run jointly defined hypotheses on our Clinical Machine Learning Platform. This multi-faceted, data-driven approach allows us to create AI models that are not only cutting-edge but also tailored to solve real-world healthcare challenges.

Conclusion

AI has incredible potential in reshaping consumer healthcare, but not all that glitters is gold. Being well-informed and asking the right questions can help you discern genuinely transformative solutions from those simply riding the AI hype train.

Further Discussion

• What are some other promising sectors where AI can make a substantial impact?
• How can consumers better understand the role of AI in healthcare?

By being critical and asking the right questions, we can foster an environment where AI's actual benefits to healthcare are realized, separating the wheat from the chaff in the process.