Healthcare has seen so many advancements over the past decade, but Chantell Preston says many people haven’t truly understood how fast AI is changing the way healthcare is delivered. “AI has revolutionized diagnostics by improving accuracy and enabling early detection of diseases. Machine learning models trained on imaging data can identify anomalies such as tumors or fractures with precision. Additionally, wearable devices like smartwatches employ AI to monitor heart rhythms, flag issues like atrial fibrillation, and promote proactive care.
As the founder and CEO of Preston Partners, she’s seeing how AI has the ability to drive personalized medicine by tailoring treatments to individual needs. By analyzing genetic, medical, and lifestyle data, AI platforms recommend therapies that are more effective and have fewer side effects. Predictive models are further optimizing care by forecasting patient responses to treatments, reducing trial-and-error prescribing.
Preston also highlights the role virtual health assistants play. Powered by conversational AI, these VAs are transforming patient engagement by offering 24/7 support for symptom checks, medication reminders, and chronic disease management. “These tools empower patients, assist in the healthcare labor shortage, and extend healthcare access to underserved regions, bridging gaps in care delivery,” she says.
With all the exciting possibilities that AI brings, it doesn’t come without its challenges. Preston says despite its immense potential, AI adoption in healthcare faces significant hurdles that must be addressed to fully realize its benefits.
“One major concern is data privacy and security. AI systems process vast amounts of sensitive patient information, making them vulnerable to breaches and unauthorized use,” Preston says. “Compliance with regulations like HIPAA in the U.S. and GDPR in the EU is essential to safeguard this data, but the risks remain significant, underscoring the need for robust cybersecurity measures and strict regulatory adherence.”
Another challenge, she says, is algorithmic bias and its impact on equity in healthcare. “AI systems often inherit biases from unrepresentative training datasets, which can lead to disparities in diagnosis and treatment. For example, tools trained predominantly on one demographic group may underperform for others, exacerbating existing inequities in care.” Addressing this issue requires the development of diverse and representative datasets and implementing mechanisms to detect and mitigate bias to ensure equitable healthcare outcomes.
Preston says ethical and legal accountability is another critical area of concern. Determining responsibility for AI errors is complex, particularly when an AI system misdiagnoses a condition or suggests inappropriate treatment. Questions arise about whether liability should fall on the developer, the healthcare provider, or the institution deploying the technology. Furthermore, ethical dilemmas emerge when AI systems operate with minimal human oversight, especially in high-stakes situations involving life-or-death decisions. Clear legal frameworks and ethical guidelines are essential to address these issues.
“The reliability and accuracy of AI systems also pose challenges. Despite their advanced capabilities, AI tools are not infallible and can produce false positives or negatives. These errors can lead to unnecessary interventions or missed diagnoses, potentially compromising patient safety. Rigorous clinical validation and continuous monitoring are necessary to ensure that AI systems perform reliably across diverse, real-world scenarios,” says Preston.
Additionally, Preston acknowledges that integrating AI into existing healthcare systems presents significant hurdles. Many facilities rely on outdated infrastructure that is incompatible with modern AI technologies. Bridging this gap requires substantial investments in system upgrades, staff training, and workflow redesigns. Resistance to change and the high costs associated with these efforts further complicate the process, creating barriers to widespread adoption.
Preston explains, “Addressing these challenges will require collaborative efforts among healthcare organizations, policymakers, and AI developers. Implementing advanced security measures, mitigating bias, establishing clear accountability frameworks, validating AI tools rigorously, and investing in infrastructure and workforce training will be crucial.”
She adds that by overcoming these obstacles, the healthcare industry can fully leverage the transformative power of AI, ensuring its benefits are delivered equitably and safely to all patients.
“The integration of AI in healthcare represents a paradigm shift, blending human expertise with machine efficiency to improve patient care. However, realizing AI’s full potential requires addressing its challenges head-on,” says Preston.
Preston believes that by investing in robust privacy protections, diverse training datasets, ethical frameworks, and workforce training, healthcare organizations can create a future where AI enhances outcomes without compromising safety or equity.
Preston concludes, “AI offers a promising path forward, revolutionizing diagnostics, personalizing care, and optimizing operations. With careful implementation and oversight, it can transform healthcare into a more efficient, equitable, and effective system, ultimately benefiting patients.”
