The rapid advancement of AI has fueled discussions about its potential to replace developers, particularly in industries like healthcare, where innovation and efficiency are critical. However, while AI tools can automate specific tasks, they cannot replace the expertise, intuition, and security-focused approach of skilled software engineers. Much like how the iPhone turned everyone into a photographer but didn’t replace professional photographers, AI enhances workflows but doesn’t eliminate the need for human oversight.
According to a Q1 2024 McKinsey healthcare and AI survey, more than 70 percent of respondents from healthcare organizations (including payers, providers, and healthcare services and technology (HST) groups) say that they are pursuing or have already implemented gen AI capabilities.
In healthcare IT, where systems manage sensitive data and demand precise, deterministic outcomes, the role of human developers becomes even more important. It’s important to strike a balance between leveraging AI-driven automation and preserving human-led innovation. Here are some critical lessons for healthcare leaders navigating this delicate intersection of efficiency and privacy.
AI Enhances Efficiency, but Developers Build Secure, Compliant Systems
AI tools excel at automating repetitive tasks—like data extraction, system monitoring, and even elements of diagnostics. While this speeds up workflows, these efficiencies depend on secure, well-architected systems that only human developers can build. Developers provide the foresight to ensure that healthcare IT infrastructure adheres to industry regulations like HIPAA, while maintaining flexibility to adapt to emerging needs.
Unlike other industries, healthcare cannot afford risks from system failures or breaches. Without strong engineering fundamentals, AI adoption may expose vulnerabilities or create systems that lack long-term viability. Skilled developers ensure AI tools are securely integrated and compliant with regulatory frameworks.
AI’s Lack of Determinism Poses Risks for Healthcare Systems
In healthcare, deterministic outcomes are essential. AI systems, particularly large language models (LLMs), rely on probabilistic outputs rather than predictable, linear responses. While AI can analyze patterns and assist in diagnostics, it cannot replace the reliability required for clinical decision-making, billing, or patient data management.
Consider the consequences of over-relying on AI for diagnostics. A subtle error—however rare—could lead to misdiagnoses, treatment delays, or patient harm. Developers bring the critical thinking needed to integrate AI solutions without compromising the deterministic reliability healthcare systems demand.
For Healthcare Data, Security Is Non-Negotiable
Healthcare data is some of the most sensitive information an organization can manage. Feeding patient data into AI systems, especially LLMs, raises significant privacy concerns for patients. Many AI tools retain data to improve performance, creating the risk of third-party leaks or inadvertent breaches. Without proper safeguards, organizations could unknowingly expose personally identifiable information (PII) or protected health information (PHI).
Traditional healthcare systems are designed to be highly auditable. In the event of a data breach, companies can trace exactly when, where, and how data was compromised. Modern AI systems lack this transparency. A breach involving an LLM could have unpredictable repercussions, and tracing the origin of such an incident would be exponentially more difficult.
To mitigate these risks, healthcare leaders must implement clear AI policies that articulate how AI tools are used, what data can be fed into systems and How developers and vendors manage and store sensitive information
Without rigorous policies, healthcare organizations risk non-compliance, regulatory penalties, and significant reputational damage.
The Web3 Cautionary Tale – Hype Without Oversight
The collapse of FTX and the downfall of Sam Bankman-Fried serve as a warning for industries adopting transformative technologies without adequate safeguards. In Web3, unchecked hype, poor governance, and overpromising led to a loss of trust, setting the industry back years.
Healthcare IT cannot afford a similar scenario. If an AI-related security breach were to occur (particularly one involving sensitive patient data) it could provoke restrictive legislation, halting innovation across the industry. Developers act as the safeguards against such failures. By applying technical discipline and keeping things in tech, business leaders can work towards AI solutions that are built responsibly and with long-term stability top of mind.
For healthcare leaders, the lesson is clear: over-reliance on AI without human accountability can have catastrophic consequences. Balance is key.
Human-Centered AI Development Is Critical for Growth
The best implementations of AI in healthcare are those where human developers play a central role in guiding, building, and monitoring systems. Developers ensure that AI solutions comply with security regulations and are able to deliver trustworthy outcomes.
By integrating AI thoughtfully, developers empower healthcare organizations to streamline workflows, improve and enhance patient care all without sacrificing security or reliability.
Healthcare leaders must continue to invest in their development teams, ensuring they have the skills to incorporate AI responsibly. This means fostering a culture where AI tools are seen as collaborators, not replacements, and equipping teams with the training they need to balance innovation with risk management.
Healthcare Developers Remain Indispensable
AI is transforming healthcare IT, offering incredible potential to automate workflows, improve efficiency, and support better patient outcomes. However, developers remain the foundation of secure, compliant, and innovative systems.
As we’ve seen with the rapid boom and bust of Web3, technology alone is never the answer. Human ingenuity, technical expertise, and accountability are essential to guiding emerging technologies like AI. Healthcare organizations must strike a balance, leveraging AI’s strengths while preserving the irreplaceable role of developers, to achieve sustainable, human-led growth.
About Mike Rispoli
Mike Rispoli is the Co-Founder and Chief Technology Officer at Cause of a Kind. With over ten years of experience designing, building, and maintaining web applications, Mike has both built and led software teams to build applications in artificial intelligence, e-commerce, mar-tech, and native mobile apps. Over a long career, and now a 2X CTO, he has developed his talent at digital agencies, early SaaS startups, and enterprise level brands. Mike thrives in an environment where complex business requirements need to be taken and broken down into elegant technology solutions.
The Cause of a Kind team has worked with notable brands including Hospital for Special Surgery, GXVE Beauty by Gwen Stefani, L’Oreal, Hilton, Marriott and Crate & Barrel’s Hudson Grace.
