Artificial Intelligence in Healthcare: Challenges and Risks

Artificial intelligence (AI) is becoming an increasingly important part of healthcare. Demand continues to grow as providers look for ways to improve patient care, increase efficiency, manage data, and address staffing shortages.

AI is now used in areas such as clinical decision support, diagnostic imaging, telehealth, patient flow management, surgical systems, and medical research.

As AI technologies continue to evolve, so will the regulations, standards of care, and best practices surrounding their use. While AI offers meaningful opportunities, it also introduces new risks that healthcare organizations must carefully evaluate.

Balancing potential with reality

AI is often presented as a solution that can dramatically improve healthcare delivery. However, similar to earlier technologies such as electronic health records, early enthusiasm can sometimes overshadow practical limitations, operational challenges, and ethical concerns.

Healthcare organizations should approach AI with thoughtful evaluation rather than unrealistic expectations. Careful planning, oversight, and risk awareness can help prevent overreliance on technology and unintended consequences.

Key challenges and risks to consider

Biased data and system limitations

AI systems rely on data and algorithms created by humans. If training data is incomplete or biased, AI outputs may be inaccurate or unfair. Differences between training environments and real-world use can also affect reliability and outcomes.

Black-box decision making

Many advanced AI systems do not clearly explain how decisions are made. This lack of transparency can raise concerns related to patient safety, clinical judgment, accountability, and liability.

Automation bias

Healthcare professionals may place too much trust in AI recommendations, even when they conflict with clinical judgment or available evidence. This overreliance can increase the risk of diagnostic errors or delayed care.

Data privacy and security

AI systems often require large volumes of sensitive health information. This creates added challenges around data protection, cybersecurity, and compliance with federal and state privacy regulations.

Patient expectations

As AI becomes more visible in healthcare, patient perceptions and expectations may change. Questions remain about trust, transparency, and how technology may affect the provider–patient relationship.

Training and education

The use of AI introduces new educational needs for healthcare professionals. Training must extend beyond technical operation to include understanding limitations, risks, ethical concerns, and appropriate clinical use.

In summary

AI has the potential to improve healthcare delivery, efficiency, and patient outcomes. At the same time, it presents new operational, legal, and risk management considerations.

Healthcare organizations adopting AI should remain aware of its capabilities and limitations while implementing appropriate safeguards, oversight, and training. A measured approach can help organizations benefit from innovation while reducing exposure to unintended risks.

Protect your organization

If your practice or organization is using or considering AI technologies, speak with your independent insurance agent at Risk Strategies ICNJ. We can help review your coverage and risk management strategy to ensure you are prepared for the evolving challenges associated with emerging healthcare technologies.

Source: https://resource.medpro.com/artificial-intelligence-challenges-risks