Disadvantages of AI in Healthcare: What CTO Should be Aware of (+ Real Case and Security Expert Predictions)

It all brings risks that tech leaders cannot ignore.

We’re not asking ourselves anymore if AI will transform medicine. We seek an answer to whether the healthcare industry can manage its dark side, including issues around AI ethics,  before it’s too late.

If you’re responsible for digital health strategy, system security, or AI deployment, this article includes a systematic review that may be useful for you. We gather insights that will help you understand what to watch out for before scaling healthcare AI adoption. You can also find a use case of AI technology in a real healthcare project and predictions on the future of Artificial Intelligence from our security expert.

Key takeaways

• For security specialists, the biggest issue is data privacy concerns: how sensitive patient data is stored, shared, and protected from breaches. They are also involved in addressing ethical challenges and introducing Artificial Intelligence training.
• For developers, the challenge lies in biased datasets, error propagation, and the “black box” nature of many algorithms. They have to find the proper ways to train AI systems and reduce the number of medical errors in the health sector.
• For CTOs and technology leaders, the disadvantages of AI in healthcare include regulatory uncertainty, over-reliance on automation, high implementation costs, and system reliability (especially in the case of clinical decision support systems and their impact on patient safety).
• For patients, AI research and automation lack the human connection patients need.
• You can trust an AI-based medical system, but with very specific safeguards. Use it as decision support and clinical research, but not a replacement. Strong security and controls against data breaches, representative data, and clinician oversight make the benefits outweigh the risks.
• AI will grow in radiology, personalized medicine, and prevention. Doctors’ roles will shift to supervising algorithms, not being replaced. Proper medical research still needs human intelligence, even with the most advanced Deep Learning algorithms.

Prevalence of AI in the Healthcare Industry

Artificial Intelligence affects everything from research labs and clinical decision-making to routine administrative tasks. Large health systems and smaller practices, academic institutions and research centers actively – all of them integrate AI where it directly eases workloads or improves care quality.

Before moving to the cons of AI in healthcare, it is important to understand the numbers and stats behind AI adoption. Let’s take a closer look at them.

Market expansion and investment

The market for healthcare AI has grown sharply over the past two years. The main drivers are advances in deep learning, Machine Learning, Natural Language Processing, and Computer Vision. Adoption is fueled by pressure to cut costs, improve efficiency, and manage workforce shortages. The United States dominates investment, while Europe and Asia are steadily expanding adoption in imaging, predictive analytics, and operational optimization.

This surge reflects not only venture capital interest but also long-term commitments by hospitals, insurers, and technology vendors. Startups focus on niche areas like clinical documentation and predictive risk scoring, while larger players such as Microsoft, Google, and Epic are embedding AI features into core platforms.

Organizational adoption

Hospitals, payers, and research centers integrate AI into daily workflows, particularly where it can reduce administrative overhead or generate insights from large datasets. Health executives increasingly treat AI as part of their digital transformation roadmaps.

Use cases vary widely: predictive analytics to identify at-risk patient populations, chatbots to handle scheduling and inquiries, AI agents and virtual assistants, generative AI to draft medical notes. Hospitals also deploy AI for workforce optimization. It helps adjust staff schedules in real time or forecast patient admission surges.

The shift from experimentation toward scaling proven applications is prevalent.

Clinical use and diagnostics

Clinicians are among the fastest adopters of AI tools, largely due to mounting documentation requirements and the growing complexity of diagnostics. Physicians use AI-driven systems to generate encounter notes, suggest diagnostic possibilities, and provide treatment options based on medical knowledge and patient history. Radiology and oncology have seen the earliest breakthroughs, with AI systems analyzing imaging scans for anomalies and assisting medical professionals with cancer detection.
The rise of AI medical note-taking tools illustrates how quickly adoption spreads when there’s a direct benefit to clinicians. In diagnostics, AI algorithms are increasingly treated as a “second reader.

What are the Key Disadvantages of AI in Healthcare?

AI is transforming healthcare, but its adoption is not without risks. While benefits like efficiency and improved diagnostics dominate headlines, the drawbacks are equally important for decision-makers to consider. Below are the most critical disadvantages shaping current debates.

Data privacy and security risks

Healthcare data is highly sensitive, and its privacy is one of the most disturbing disadvantages of Artificial Intelligence in healthcare. AI systems require large volumes of patient information to train and operate effectively, but storing and processing this data raises serious privacy concerns.

Because many algorithms work as “black boxes,” security concerns multiply. It is not always clear how or where the data is being used, and this increases the risk of security breaches and data misuse.

• Patients may be unaware that their data is shared with third-party AI vendors.
• Cyberattacks targeting AI systems could expose millions of health records.
• Compliance with the Health Insurance Portability and Accountability Act (HIPAA), General Data Protection Regulation (GDPR), and similar regulations is complex when AI systems continuously process personal data.

Case in action

The 2024 Courier Mail investigation researched AI-powered medical scribes being used in Australian clinics. These tools are designed to listen during patient consultations and automatically generate clinical notes. While they promise to save time for doctors, the report highlighted two key risks.

Firstly, these tools sometimes produced inaccurate or fabricated details in the medical record. This not only creates clinical safety risks but also undermines the reliability of stored patient data.

Secondly, because these scribes process sensitive conversations in real time, patient data may be transmitted, stored, or even used to retrain commercial AI models. The lack of clarity about where the data goes, who has access, how securely it is handled, and whether informed consent was obtained raised red flags among clinicians and privacy experts.

Potential for misdiagnosis and inaccuracies

AI models can appear confident even when they are wrong, and this is one of the serious problems with AI in healthcare. A misclassified image or an incomplete dataset may result in a false diagnosis. Unlike traditional clinical tools, many algorithms cannot explain why they reached a specific conclusion, making errors harder to detect.

• Incorrect outputs can mimic the “symptom checker” effect, where benign symptoms trigger serious but false diagnoses.
• Errors in training data, such as mislabeled scans, can cascade into flawed patient outcomes, sometimes with dangerous consequences.

Case in action

In 2024, The Verge reported that Google’s Med-PaLM (later Gemini) hallucinated a non-existent medical term (“basilar ganglia”) due to a typo in training data. As you can see, simple data errors can cascade into faulty recommendations.

Bias in training data

AI systems are only as good as the data they learn from. If datasets are skewed toward specific demographics (for example, predominantly one ethnicity or age group), the models risk amplifying bias. This can lead to unequal treatment quality across patient populations and may exacerbate issues related to human error.

Based on this, there are two main negatives of AI in healthcare in this context:

• Biased data has been shown to reduce accuracy in diagnosing conditions among underrepresented groups.
• Reliance on non-representative clinical samples risks deepening existing healthcare disparities.

Case in action

Biased responses make health equity a real concern.

Here’s the example. IBM heavily promoted Watson Health as an AI cancer-treatment assistant that could revolutionize cancer care by recommending treatments.

In practice, the system often produced unsafe or irrelevant suggestions, based on limited and sometimes flawed training data. Hospitals abandoned the tool, and IBM eventually sold off Watson Health in 2022 after years of disappointing results.

Over-reliance and the need for human oversight

AI should support, not replace, clinical decision-making. But when doctors or patients place too much trust in algorithms, critical thinking can erode. While significant cost savings can be achieved, over-reliance increases the chance that errors slip through unnoticed, while a lack of oversight makes it harder to catch them in time.

• Clinicians may defer to AI outputs even when their own expertise suggests otherwise.
• Long-term dependence risks narrowing professional expertise.
• Patients may take AI advice as authoritative, without understanding its limits.
• A system outage could cause operational paralysis if workflows are built around AI alone.

Case in action

If staff accept algorithmic recommendations without validation, misdiagnoses or unsafe treatment decisions could follow. Here is a real-world example of the negative impact of AI in healthcare in terms of over-reliance.

A 39-year-old man from California used ChatGPT for health advice and was told to drink a mixture of salt and apple cider vinegar. After consuming it for several days, he developed hallucinations, paranoia, and violent behavior caused by sodium poisoning. Doctors treated him in the hospital for acute psychosis, and specialists later warned that AI chatbots can generate convincing but unsafe medical recommendations.

Ethical, legal, and regulatory challenges

Healthcare AI raises difficult questions about accountability and oversight. When an algorithm makes a mistake, it’s unclear who is liable: the developer, the healthcare provider, or the institution that approved its use.

Ethical concerns also arise around transparency, patient consent, and the fairness of algorithms trained on biased data. And it doesn’t end with these AI disadvantages in healthcare.

At the same time, regulators struggle to keep pace. Traditional approval frameworks were built for static medical devices, not adaptive algorithms that change over time. While the FDA has now cleared more than 1000 AI-enabled devices, ongoing monitoring and safety validation remain inconsistent.

The questions remain: How do we regulate systems that learn and evolve? Who ensures continuous compliance once they’re in the market?

Case in action

The IBM Watson Health case is also relevant here. Its failure raised ethical questions about whether companies oversold AI capabilities without proof. Regulators have yet to clarify where liability lies when these tools go wrong.

High implementation costs

AI deployment in healthcare may require some costs. Beyond licensing fees, organizations face expenses for infrastructure, data integration, cybersecurity, ongoing model retraining, etc. For smaller hospitals and clinics, these expenses are often prohibitive.

• The upfront investment in cloud infrastructure and secure storage is substantial.
• Healthcare organizations require skilled staff to manage, audit, and retrain AI systems. Professional AI development services are also not cheap.
• Return on investment is often long-term, which may not align with tight budgets and become one of the major drawbacks of AI in healthcare.

Case in action.

Forward, a high-profile healthcare startup led by Adrian Aoun, shut down its AI-powered CarePod clinics in late 2024. The pods were designed as futuristic, automated health stations, but struggled with high costs, technical challenges, and limited adoption, especially in terms of achieving regulatory approval.  Reasons included leadership and scalability, but it also illustrated the enormous costs of sustaining AI-heavy healthcare operations.

Integration and reliability challenges

AI systems must integrate seamlessly with existing electronic health record (EHR)/ electronic medical records (EMR) platforms, lab systems, clinical workflows, and so on. Poor integration can frustrate clinicians rather than support them.

Reliability is also critical: an unstable or frequently malfunctioning tool is also one of the disadvantages of using AI in healthcare. It can reduce trust and adoption.

• Integration delays are common, especially in legacy healthcare IT environments.
• Frequent system “hallucinations” or incorrect outputs damage user confidence.
• Reliability issues may force staff to double-check outputs, negating efficiency gains.

Lack of personal touch

AI systems can analyze data, generate notes, and even answer patient questions. But they cannot replicate the empathy or nuanced understanding that comes from human interaction. For patients, especially those in vulnerable conditions, this lack of human connection can make care feel transactional. This is also one of the negative effects of AI in healthcare.

For instance, AI chatbots used for triage can deliver technically correct advice but often miss the emotional context a human nurse or doctor would recognize. This can affect patient trust and satisfaction.

Can We Trust AI in Healthcare Despite Its Disadvantages?

AI in healthcare should not be treated as infallible, but it can be trusted when used with safeguards. Its drawbacks are well-documented, and each has a practical path to mitigation. Strong data governance reduces the risk of breaches, representative training sets limit bias, human-in-the-loop validation prevents misdiagnoses, careful cost–benefit analysis helps providers adopt only where impact justifies expense… The list is long but precise.

The advantages are measurable. AI shortens radiology reporting time, automates note-taking, supports early cancer detection, and improves staff scheduling in busy hospitals. There is a special place for AI in clinical data management. These gains don’t eliminate the need for physicians’ expertise, of course. However, they free up capacity and improve accuracy when oversight is in place.

So, can AI be trusted? Yes, but only if it is applied as a support system rather than a replacement for clinical judgment, and if providers commit to transparency, oversight, and regulation. In that context, the benefits outweigh the risks. In this case, AI and ML analyze patient data in the best way and meaningfully improve patient outcomes when their limitations are actively managed.

How to Deal With the Cons of Artificial Intelligence in Healthcare?

Let’s try to discuss this matter in different contexts: for security specialists, for developers, and for tech leaders.

Trust through security

For security specialists, trust depends on airtight data protection. Use a HIPAA-compliant LLM so that patient records and conversations are processed under strict legal standards. Apply end-to-end encryption and zero-trust access controls to reduce the attack surface.

There are two other security measures to be aware of:

• Audit third-party AI vendors regularly to verify how they store and process patient data.
• Deploy real-time intrusion detection systems to catch suspicious activity before breaches escalate.

Better data quality and transparency for developers

For developers, the main issue is data quality and explainability. They have to validate training sets with diverse and representative clinical data to reduce bias. Your team can also integrate explainable AI frameworks so that outputs include reasoning or traceable references, not just results.

Also:

• Apply continuous retraining with updated datasets to prevent models from drifting over time.
• Use synthetic data generation to safely expand datasets without exposing sensitive patient information.

Responsible scaling for CTOs and technology leaders

For CTOs and executives, trust comes from scaling AI responsibly and sustainably. Treat AI as decision support, not decision replacement, and always keep clinicians in the loop.

A good solution is to pilot solutions in limited workflows before rolling out system-wide. Always calculate the total cost of ownership (infrastructure, retraining, monitoring) before greenlighting deployments.

The other important thing is to build failover protocols so clinical operations continue if the AI applications go offline. And last but not least, require vendor transparency in model performance, bias testing, and error reporting before adoption.

How We Turn AI Into a Safe, Practical Healthcare Solution: Real Case

We know both the potential and the negative impact of Artificial Intelligence in healthcare. And we always, always apply it with special caution and focus only on use cases where the benefits are clear and the risks can be managed in advance. At the end of the day, the balance lies in making sure AI improves healthcare workflows without creating new vulnerabilities. So, privacy and compliance must always be at the core.

Working properly with AI means the right approach for each case. That includes specialized model training and fine-tuning to achieve accurate results for medical tasks, and when privacy is critical, relying on open-source models and self-hosting to keep sensitive data under full client control, and much, much more.

Case in action: Tiro.Health

With Tiro.Health, we built a web platform that simplifies clinical workflows by automating the creation of medical documentation templates. The solution uses AI to generate and update form structures quickly, but it never processes or stores patient data. That’s what makes this digital healthcare product completely safe from a compliance perspective.

We worked on the platform with healthcare professionals in mind. Its modular design lets clinicians customize forms to match their specific needs, while intuitive workflows reduce time spent on routine admin tasks. As a result, this tool saves hours of paperwork and lowers cognitive load, without compromising privacy.

Tiro.Health project shows what’s possible when AI is applied responsibly:

• faster and more accurate documentation;
• streamlined processes;
• more time for patient care;
• compliance and safety of patient and medical data.

We build AI healthcare solutions that speed up workflows, cut administrative burden, and improve care. They are fully compliant with privacy standards. Whether you’re looking to design safer AI use cases, integrate medical tests, fine-tune models for clinical tasks, or deploy self-hosted systems to protect sensitive data, we can help you do it right from the start.

What’s Next with AI in Healthcare?

By Victoria Shutenko, an Experienced security engineer and web app penetration tester, AWS Community Builder.

When it comes to predictions about AI, transparency and explainability will be key. We need tools that let doctors see the logic behind AI’s recommendations. Decisions are leaning more and more on GPT-based models, and if the reasoning isn’t visible, doctors risk paying less attention. Keeping that logic in front of clinicians all the time is important.

Data quality matters just as much. Expanding datasets, including diverse populations, and keeping bias under control are obvious next steps, but they’re essential if we want fair results for every patient.

Regulations and standards are coming fast. There are already thousands of startups, and without clear rules, it’s hard to tell which solutions are truly validated. International protocols from the FDA, EMA, and others will set the bar, so we can separate what’s certified and safe from what’s still experimental.

And AI should never be about replacing people. The best results come when it supports doctors, not substitutes for them. Decision support, not decision making. A tandem of human + algorithm is where the real value lies.

Looking ahead 5–10 years, AI will likely become standard in radiology, pathology, personalized medicine, and risk prediction. We won’t see doctors pushed out, but their role will shift from diagnosticians to supervisors of algorithms.

The biggest innovations will be in prediction, prevention, and clinical trials, not only diagnosis. And yes, more startups will appear, more failures too, which means more limits, certifications, and oversight will follow.

FAQ

1. What are the negatives of using AI in healthcare?

   Health care AI can raise serious issues around data privacy, bias, errors, and costs. Patient data may not always be stored or used securely. Algorithms trained on limited datasets can produce biased results that don’t apply to all patient groups. 

   AI can also generate inaccurate or fabricated outputs in clinical practice, which may mislead clinicians or patients and compromise the health care system. Finally, the high cost of implementation makes adoption difficult for smaller providers and health care professionals.

2. What is the biggest challenge of AI in healthcare?

   The biggest challenge for medical practitioners is trust and data privacy in AI-based systems. Healthcare leaders must balance the promise of medical AI with the risks of inaccurate results, unclear accountability, and health data security concerns. Building trust in healthcare systems requires strong oversight, solid security controls, transparent algorithms, regulatory clarity, and seamless integration with existing clinical systems.

3. Can AI make mistakes in healthcare?

   Yes. Even trustworthy medical Artificial Intelligence can produce confident but wrong answers – a problem often called “hallucination.” For example, symptom checkers may suggest severe conditions for minor complaints, or diagnostic tools may misclassify scans during various medical tests if the training data was flawed.

   These mistakes show that medical education is still very important for future health professionals, and why Artificial Intelligence AI should support, not replace, medical practice and clinical judgment in health care.