Humans: The Make or Break for AI in Healthcare

I've spent years observing the healthcare landscape, watching innovations come and go. And now, we stand at the cusp of something truly transformative: artificial intelligence.

AI promises to revamp diagnostics, personalize treatments and streamline operations in ways we could only dream of before. It’s a fantastic time, and I get excited about the possibilities. But here's what keeps me up at night: are we, the humans in healthcare, ready for this revolution? I fear we may be the “thing” that slows down or stops this amazing technology's potential.

The Promise of AI and the Reality of Implementation

AI’s potential in medicine is vast. We’re talking about AI algorithms that can analyze medical images faster and more accurately than any radiologist, predictive models that identify at-risk patients before they get sick, and robotic assistants that can precisely perform surgeries. It all sounds like a science-fiction movie. However, having the technology is not enough. It’s about the acceptance and use of the technology.

Real-World Example: We have AI tools that can detect diabetic retinopathy with high accuracy. Yet, many clinics aren't using them. Why? Sometimes, it’s because of cost. But often, it’s because of hesitation among healthcare providers, fear of being replaced and not knowing how to integrate these tools into daily work. There is a learning curve.

And that’s the problem I’m talking about. The technology exists, but if the systems and people that are meant to use it are not ready, the incredible benefits will never be fully realized.

Related Article: 5 AI Case Studies in Health Care

The First Obstacle

One of the biggest challenges I see is our mindset. The healthcare world has always been very personal. We rely on experience, intuition and the direct connection between doctor and patient. It is understandable why introducing AI seems to disrupt this process. Some medical professionals might worry that AI will take away their jobs, decrease the patient’s personal touch or make them feel like they don’t have control. I understand it, but it needs to be changed.

We need to view AI as a colleague, not a competitor. Think of it as a highly skilled assistant who can do the heavy lifting while we focus on what we do best: the art of medicine. AI can handle many tedious tasks, so healthcare professionals can spend time with patients.

Case Study: A study by Stanford University showed that AI algorithms could diagnose skin cancer with similar accuracy to board-certified dermatologists. This should not make the dermatologist redundant. This should mean that the doctor has more time to treat patients and conduct further research. It’s about using AI to improve care, not just replace jobs. According to research published in Nature Medicine, AI tools can increase the efficiency of healthcare processes, which means that the same number of doctors can treat more patients with better precision.

Building the Bridge

We need to focus our efforts on this area: Training healthcare professionals to work with AI so they understand its capabilities and limitations. That means integrating AI literacy into our education programs and workshops. This includes data interpretation, algorithm bias and the ethical implications of using AI in healthcare. Also, the general public will need to be educated about AI.

Anecdote: A while back, I talked with a group of nurses. They were curious about using AI to predict patient falls. Once they understood that AI was a tool to make their jobs easier and help them prioritize their patients instead of a replacement, they started to get excited about the possibilities. But this understanding came from training and education.

Laying the Foundation

AI thrives on data. It needs reliable, structured, comprehensive data to learn and make good predictions. We must improve our data collection systems and build the necessary infrastructure to support these new technologies. This means ensuring we build AI with the correct data and looking for bias. This is not just technical stuff but the proper and responsible use of AI.

Real-World Example: In a case study published in The Lancet Digital Health, a hospital that invested in infrastructure for AI-powered diagnostic tools saw a 30% reduction in diagnostic errors. Properly using technology and data is an investment in the future of healthcare.

Collaboration Is The Key to Success

AI in healthcare will not be a solo effort. It will require open communication between healthcare professionals, technologists, ethicists, policymakers and patients. We need to create spaces where we can openly discuss the potential and the drawbacks of using these new tools, to find solutions and implement them with care and thoughtfulness. This includes discussions around transparency, accountability and the ethical use of AI in healthcare.

Anecdote: At a recent conference, clinicians, AI specialists and patients were in the same room. They were looking at using AI to predict patient risks. Incorporating patient feedback into the algorithms could significantly improve the model's accuracy. This made it clear that a good team approach is the key.

Time to Lead the AI Charge

It's time for us, healthcare professionals, to become more than just users of AI. We need to be the leaders. We need to shape the use of this new technology to improve patient care, and not just for innovation's sake. We must be the advocates for the patients to guarantee their rights are protected.

We need to proactively invest in education, embrace new ways of working and build an environment of open communication. If we fail, we are not only limiting the technology but also limiting our profession.

Let’s not be the constraint on our industry’s future. Let's be its champion!

The potential for AI in healthcare is incredible. But it will be us, the humans, who determine the level of success or failure of this new technology. By adopting the correct mindset, improving our data systems and being open to change, we can pave the way for a future of healthcare that is more efficient, more effective and more focused on the well-being of the patient.