Ignoring artificial intelligence (AI) isn’t neutral — in fields like medicine, law, engineering and education, failing to use it may increasingly count as malpractice, neglect or professional misconduct.

In a world in which AI is becoming dependable, efficient, and increasingly embedded in professional workflows, the omission of AI tools is no longer merely conservative—it may be negligent. Professionals who continue to operate as though “business as usual” suffices risk falling below evolving standards of care, putting their clients, patients or students at risk—and themselves liable.

The Case: Why Ignoring AI Is a Risky Gamble

The evolving standard of care

In disciplines governed by professional standards (medicine, litigation, teaching, engineering), the “standard of care” is what a reasonably competent practitioner would do under similar circumstances. With AI tools becoming part of many professionals’ toolkits, the bar is shifting.

For example, in healthcare the literature already flags that reliance on AI-assisted diagnosis and predictive analytics can improve outcomes—and failure to consider them may soon be viewed as falling short of the standard of care (Jassar et al., 2022). 

Likewise, healthcare risk-analysis pieces emphasise that if AI is available and validated but a clinician fails to use it, the decision not to use it “could expose liability” (Parsons Behle & Latimer, 2024). 

Technology is both capable and expected

AI tools now perform at or above human levels in certain domains. For instance, diagnostic imaging, predictive risk modelling, and data‐driven decision tools are in widespread use. When a human professional declines to apply a tool known to improve accuracy and reduce error, they are effectively choosing a slower, less reliable method—and clients may suffer.

In law and litigation, engineers and educators alike, AI is beginning to support research, automate executive tasks, surface risks, personalize learning. Ignoring these means you are operating at a disadvantage compared to peers who do.

The liability gap: omission as misconduct

Medical-legal scholars note that the failure to adopt relevant AI when it is reasonably available may expose professionals to claims of negligence, especially in “safety-critical” domains such as medicine. Literature on AI in healthcare points to “moral accountability” and safety assurance issues—i.e., who is responsible when a human professional had the tool but chose not to use it? 

From one review: “What if AI is available … and the radiologist or treating doctor fails to use it and a tumor is missed—is there liability?” (Parsons Behle & Latimer) 

Not just about making mistakes—but about not doing better

Malpractice is often framed as “doing something wrong”. But increasingly, it can be “failing to do what a reasonably prudent peer would do” given the state of technology. If peers use validated AI tools and you don’t, that omission may become evidence of sub-standard care.

How This Applies to Specific Professions

Here’s how the risks manifest in key fields:

Medicine & Healthcare

• AI-powered diagnostic and predictive tools now assist in imaging interpretation, risk stratification, and treatment planning.  
• If a clinician chooses not to use validated AI tools (or ignores them) and a harmful outcome results, questions will arise about whether they met the evolving standard of care—even if they followed “traditional” methods.
• Professional liability articles emphasis that the “black box” nature of AI complicates accountability, but also highlight that failing to deploy AI when available may be considered neglect.  

Litigation & Legal Practice

• Attorneys using AI for document review, legal research, e-discovery are gaining efficiency and accuracy. A lawyer who refuses to adapt and misses a key precedent or fails to identify risk may face complaints of incompetence.
• While direct case law is still nascent, the general standard in legal professions is “competent representation according to the standards prevailing in the profession.” If your peers use AI and you don’t, you may fall below that standard.

Engineering & Design

• Engineering now leverages AI for predictive design, safety analysis, optimization, simulation. Engineers who ignore available AI tools may design structures less safe or less efficient than peers—and could face professional discipline or liability for failing to employ best practices.
• Regulatory bodies increasingly expect engineers to stay current with tools that impact safety, reliability and risk mitigation.

Teaching & Educational Practice

• In education, AI is being used for formative assessment, personalized learning paths, data-driven intervention. Teachers or administrators who ignore these tools may fail to deliver on “least-restrictive environment” or differentiated instruction obligations.
• If schools adopt AI tools and some teachers refuse to integrate them (to the detriment of student outcomes), that may raise concerns of neglect or failure to meet professional expectations.

Why This Is Malfeasance or Neglect — Not Just “Conservative Practice”

• Availability of validated AI tools: When a tool is validated, widely adopted, and known to reduce error or increase safety, failure to use it may cross from “option” to “obligation.”
• Professional standards evolve: Standards of care are not static. As technology becomes embedded, they change. What was optional yesterday may become expected tomorrow.
• Duty to adapt: Professionals have a duty to maintain competence. Courts and licensing boards expect professionals to stay reasonably current with advances. Ignoring AI may violate that duty.
• Risk of harm from omission: Failing to use helpful AI can directly result in harm—delayed diagnosis, inadequate design, inferior instruction. Such harm may trigger liability for neglect.
• Ethical obligation: Professionally, ignoring tools that improve accuracy, safety or equity may conflict with ethical commitments—e.g., “do no harm” in medicine or “provide competent service” in engineering and law.

Three Action Research Scenarios to Test Your Profession’s Readiness

Study 1: Medicine – “AI-Assisted Diagnosis vs Traditional Process”

Research question: Does use of validated AI diagnostic support reduce diagnostic error, and what are the outcomes if it is not used?

Plan: In a hospital department, split comparable cases into two groups: one uses AI-assisted tools for imaging/diagnostics, the other uses traditional methods without AI.

Measures: Diagnostic accuracy, time to diagnosis, adverse outcomes; compare standard‐practice group to AI group.

Hypothesis: The AI-assisted group will yield fewer missed diagnoses and faster intervention. Failure to use AI may correlate with higher error rates—a red flag for negligence.

Study 2: Engineering – “AI Predictive Analysis In Design Safety”

Research question: When engineers do vs do not incorporate AI predictive modelling for safety/structural risk, what are the differences in outcome and peer review findings?

Plan: Over one design cycle, one team uses AI tools for safety simulation and optimization; another team uses traditional analytic methods without AI.

Measures: Number of design revisions, peer review safety flags, cost/time of project, safety margin of final design.

Hypothesis: The AI group will achieve higher safety margins and fewer peer-review flags. The non-AI group may be deemed less robust, potentially negligent in professional review.

Study 3: Teaching – “Personalized AI Learning Intervention vs Traditional Differentiation”

Research question: Does integrating AI-based adaptive learning platforms improve student outcomes relative to traditional differentiation methods, and does non-use constitute failure of professional instructional practice?

Plan: In comparable classrooms, one teacher uses an AI adaptive learning system to tailor interventions; another teacher uses only traditional instruction without AI support.

Measures: Student growth on standardized tests, engagement metrics, intervention-need reduction, teacher workload; also, peer review of instructional practice.

Hypothesis: The AI-integrated group will show higher growth and engagement. The non-AI teacher may be flagged for failing to adopt evidence-based tools—raising questions of neglect of student needs.

Implementation Guide & Professional Guardrails

1. Inventory available AI tools in your field: Identify validated AI applications relevant to your profession (diagnostic systems, design optimization, adaptive learning platforms, legal-research assistants).
2. Assess peer adoption: Determine whether peers or organizations in your field are adopting the tools. If yes, non-use becomes more conspicuous.
3. Establish adoption thresholds: Work with professional bodies or institutional leadership to set timelines for training, pilot use, and full integration of AI.
4. Document reasoning for non-use: If you choose not to use a given AI tool (due to cost, suitability, risk), document your rationale—so you’re not later judged to have omitted without justification.
5. Train on AI’s strengths & limitations: Use is not passive. Professionals must understand when AI can be trusted, when human judgment is required, and how to supervise AI output.
6. Informed consent / client disclosure: In fields involving clients or patients, disclose use (or non-use) of AI tools and explain implications. Failure to inform may compound liability. (see e.g., “informed consent” concerns in medical AI literature)  
7. Continuous review & audit: Monitor outcomes with and without AI tools, document improvements, and adjust practice accordingly. If evidence shows AI improves outcomes, non-use becomes harder to defend.

Conclusion: The Time for “Maybe Later” Is Over

In professions where lives, safety, equity, and critical decision-making are at stake, the omission of AI isn’t simply a “wait and see” posture—it may become a hallmark of neglect or malpractice. The landscape is shifting: professionals are increasingly expected not only to know about AI, but to use it where validated.

If you are a clinician, teacher, engineer or lawyer who is still operating as though the tools of 2010 suffice for 2030, you are risking more than inefficiency—you are risking professional sanction, liability, or worse. The moment to act is now: evaluate, adopt, document, train—and ensure that your professional practice remains defensible in a world where AI is no longer optional.

References (APA 7th Edition)

Abràmoff, M. D., et al. (2024). Defining medical liability when artificial intelligence is applied on-label and off-label. [Journal]. (see discussion on liability and AI) 

Habli, I., Lawton, T., & Porter, Z. (2020). Artificial intelligence in health care: accountability and safety. BMJ Quality & Safety, 29(6), 474-481. 

Jassar, S., Adams, S. J., Zarzeczny, A., & Burbridge, B. E. (2022). The future of artificial intelligence in medicine: Medical-legal considerations for health leaders. Journal of Medical Ethics, 48(12). 

Parsons Behle & Latimer. (2024). The risks of AI in healthcare: omission of use as potential malpractice. 

Simbo AI. (2025). Understanding the legal implications of AI in healthcare: Accountability, malpractice and the black-box dilemma.