Artificial intelligence (AI) is no longer a futuristic concept in health care; the realities of AI are rapidly becoming an integral part of diagnostics, treatment, and patient management. While the promise of AI-driven health care is vast, the utopian picture that many are painting with pen and podium glosses over the complex realities that lie ahead in the next 10 years. We’re on the cusp of a significant transformation in health care delivery, but breakthroughs and challenges will mark the journey. Here are some of the realities.
The Diagnostic Revolution
One of the most immediate and impactful applications of AI lies in diagnostics. Machine learning algorithms are excelling at analyzing medical images, such as OCTs and other retinal images detecting subtle patterns that may escape the human eye. This translates to earlier and more accurate diagnoses of diseases such as macular degeneration, diabetic retinopathy, and glaucomatous optic neuropathy. In the next decade, we can expect AI to automate routine image analysis, freeing up clinicians to focus on more complex or non-routine cases. This will lead to faster and earlier diagnoses, which may, in turn, slow the progression of diseases in their earliest stages and prevent existing diseases from advancing.
The challenge is that the clinical care community must have access to and adopt these AI-driven technologies in retinal and nerve imaging. For mass adoption of these technologies, this will take time. However, the demand exists for advances in architecture, processing speed, and data storage. We need to access data from devices all over the world, not just a few selected sites, and this will take universal device integration, which faces infrastructure, legislative, regulatory, political, and financial hurdles. And even if this occurs, we have to overcome data quality and bias issues. No small obstacle!
Wearables
It is even more interesting to think about how AI-powered handheld devices and mobile apps will change point-of-care diagnostic capabilities. Consumer wearable devices such as your watch, rings, eyewear, and maybe soon contact lenses, will be everywhere, collecting massive amounts of data. Though they are not yet designed primarily for health care, as demand grows for more personalized diagnostics, these heath care tools will be developed and released. I believe these wearables will enable remote diagnostic monitoring, which may in turn democratize access to health care and improve early detection. I see a day where AI will integrate diverse data sources, including genomics, proteomics, and patient history, to create personalized risk assessments and diagnostic profiles to give truly personalized diagnostics, and thus, enable more targeted interventions and preventative care.
However, once again, there are many infrastructure challenges to overcome. AI developers need to create a common interface for all wearables to interact with, so that this treasure chest of data is accessible. To do that, all manufacturers have to agree to some common language sets, and getting behemoths like Apple, Google, Meta, and others to play nice will be an interesting task.
The Human Element
Despite the advancements in AI, the human element remains crucial in health care. Undoubtedly, the single biggest challenge is for health care consumers to feel safe. The success of AI-driven patient management depends on addressing privacy and security concerns. The collection and analysis of sensitive patient data raise ethical questions about data ownership and consent. Moreover, the digital divide may exacerbate existing health disparities, as not everyone has access to the necessary technology and internet connectivity.
To overcome these challenges, the greatest battlefield will be for consumers to see AI as a tool to augment – not replace – clinicians in a health care setting. In the next decade, we will need to see a shift toward a collaborative model, where AI and humans work together to provide optimal patient care. Characteristics of this model would
- Allow consumers to decide what information can be shared,
- Be transparent, paramount so that all can see that AI is used effectively and ethically, and
- Enable open communication about the benefits and limitations of AI.
I believe in the next 10 years we will witness a profound transformation of AI-driven health care. While the potential benefits are immense, navigating the realities of AI implementation requires addressing challenges related to data quality, ethical considerations, and consumer adoption. By fostering collaboration, promoting transparency, and prioritizing patient well-being, we can harness the power of AI to create a more efficient, equitable, and personalized health care system. Here at AI in Eye Care, our job is to educate the eye care industry about tools being developed, how they will impact our industry, and discuss both the amazing opportunities and the challenges we must overcome to experience the potential transformation of how and where we provide care.
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