Science topics: MedicineDiseaseDiagnosticsDiagnostic Errors
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Diagnostic Errors - Science topic

Diagnostic Errors are incorrect diagnoses after clinical examination or technical diagnostic procedures.
Questions related to Diagnostic Errors
George Benneh Mensah
George Benneh Mensah
  • asked a question related to Diagnostic Errors
Who will be criminally held responsible for the misdiagnosis and mistreatment of a patient that leads to the death of the patient?
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2 answers
  • 10 Dec 2023
As an advocate and a passionate researcher on AI tools' application in law, healthcare and education, I've being wondering on the criminal liabilities associated with misdiagnosis and mistreatment causing death as medical practitioners undertake their work with AI as a tool in such practice. I've written about 4 articles and published in this realm of malpractice issues. However, my difficulty as a legal researcher and AI enthusiast makes me wonder, how this precision tool, AI in medicine and medical treatment could lead to misdiagnosis and mistreatment causing death of patient in a facility? Will the programmer, medical doctor user or the trainer of the AI be held criminally responsible for this death or they all will be severally and jointly be held criminally liable?
Let me get your views on these issues and try as much as possible if there had been some case laws in either Civil or Common Law Jurisdictions that have dealt with such issues or even pending in those jurisdictions.
Relevant answer
  • 10 Dec 2023
Answer
Analysis on the potential criminal liabilities associated with AI-enabled medical misdiagnosis or mistreatment leading to patient death:
The increasing use of artificial intelligence (AI) in medicine and healthcare brings tremendous benefits in efficiency, accuracy, and access to care. However, it also introduces complex legal and ethical issues when things go wrong. One such issue is who bears criminal liability if an AI-enabled misdiagnosis or mistreatment results in a patient’s death. This is a gray area with scarce direct legal precedent so far.
Potential Culpable Parties
There are three main parties potentially culpable in this scenario:
1. The AI developer/programmer who created the algorithm
2. The human healthcare provider who ultimately made the diagnosis/treatment decision
3. The institution/employer responsible for implementation and training of the AI system
Each plays a role in leveraging AI safely and effectively to improve patient outcomes. However, flawed actions at any point could lead to patient harm.
Legal Analysis – Criminal Culpability Principles
Criminal liability generally requires proving actus reus (guilty act) and mens rea (guilty mind). For homicide charges like negligent manslaughter or reckless endangerment, negligence or recklessness could satisfy mens rea requirements.
The AI developer may be liable if they negligently designed algorithms that performed inadequately. However, given the “black box” nature of some AI, it may be difficult to prove clear foreseeability of harm.
The healthcare provider may also be liable based on their professional duties of care toward patients. Using AI irresponsibly could constitute criminal negligence. However, not all mistakes necessarily reach that bar. Providers must balance relying on AI with their own expert judgment.
Finally, healthcare institutions have a duty to ensure patient safety and regulatory compliance. Reckless implementation of unreliable AI could incur criminal liability for the entity and its leadership. However, reasonable good faith likely provides legal cover.
Relevant Case Law and Precedents
There is little direct precedent so far regarding criminal liability for AI harms in medicine. Much likely hinges on interpreting existing negligence/recklessness principles. However, civil lawsuits around issues like computer-aided detection software defects provide some guidance. While, there is still a lack of direct legal precedent when it comes to criminal liability for AI-related medical errors and patient harm likely due to the nascent state of widespread AI adoption in healthcare. However, we can examine some analogous cases that could provide guidance:
Therac-25 Radiation Therapy Machine: Between 1985-1987, design flaws in the Therac-25 system resulted in massive radiation overdoses on several patients who later died. The manufacturers and software creators were found liable for negligence, failure to warn, and defective products. This case helps set precedent for medical device AI creators' duties of care and safety requirements.
Watson for Oncology Issues: IBM's Watson has faced criticism over some cancer treatment recommendations deemed unsafe by doctors. No known deaths have occurred, but it highlights AI oversight issues in medicine. IBM may bear responsibility for negligent design if harms did occur.
Uber Self-Driving Car Fatality: In 2018, flaws in Uber’s autonomous driving systems resulted in the first self-driving car fatality. While not a medical case, it explored important AI safety principles. Uber settled criminal negligence charges - a harbinger for medical AI.
Speculative Future Lawsuits: As AI adoption spreads, legal experts anticipate lawsuits against AI developers, health providers, and hospitals around issues like failure to validate systems, not using reasonable care with AI, and enabling faulty reliance on algorithms over physician judgment. These would likely invoke negligence/recklessness principles.
While no direct precedent exists yet, the growing understanding of AI risks means creators and users have less legal excuse for deficient safety practices or overreliance on still-imperfect systems. Medicine and technology both evolve quickly, so legal guidance typically lags behind. But the above cases help frame the eventual liability landscape regarding AI's role in causing preventable patient harm or death due to misdiagnosis/mistreatment.
However, there are some emerging legal issues pertaining to medical AI and potential misdiagnosis/mistreatment harms that will need to be addressed as well, amongst these are:
Causation Complexities
Multi-party AI development across organizations could complicate assigning culpability if harm occurs. Courts may need to determine levels of liability for various contributors.
Similarly, many parties (developers, clinical validation teams, individual users) help shape how AI gets applied in real-world settings. Untangling their contributions could prove challenging.
Regulatory Uncertainty
Global policymakers are just beginning to develop quality control standards and safety validation expectations for AI in medicine. Such regulatory guidance will shape legal duties.
We may see conflicts emerge between government rules and efforts by technology bodies to self-govern. Courts may have to resolve such conflicts after patient harm events.
Data Imbalances and Bias
If certain population groups are underrepresented in medical AI training data, it could cause higher risk of misdiagnosis for those groups. Victims could pursue fairness-based legal challenges.
Attempting to prove algorithmic bias contributing to patient harm medically and legally could be difficult initially.
Informed Consent Issues
How much providers should disclose to patients about an AI tool’s upsides/downsides remains debated. Inadequate disclosure could bolster liability if issues then occur.
Truly informed consent around still-evolving technology also presents challenges. This area needs legal clarification.
These are among the thornier issues healthcare facilities, AI developers, medical professionals, policymakers, and ethicists continue grappling with as the technology matures. There are rarely easy or unanimous answers, so legal scrutiny around these areas will evolve for years to come.
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Yahya Jirjees Salman
Yahya Jirjees Salman
  • asked a question related to Diagnostic Errors
Which sample is a choice for the diagnosis of PID due to Chlamydia infection?
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3 answers
  • 1 Mar 2023
Chlamydia trachomatis in women is symptomatic and mainly causes cervix erosion. The microorganisms are obligately intracellular and can not grow on ordinary or routine culture media. As well, it was not recognized by gram staining. It can not reside in the lower genital site; therefore, sampling and sample type are critical for misdiagnosing the cases.
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  • 10 Mar 2023
Answer
Endocervical swab: One of the most commonly used samples for the diagnosis of Chlamydia infection.
Urine sample: A urine sample can be used to detect the presence of Chlamydia trachomatis DNA using nucleic acid amplification tests (NAATs).
Blood test: A blood test can be used to detect the presence of antibodies against Chlamydia trachomatis, which can indicate a past or current infection.
Pelvic fluid sample: In some cases, a sample of fluid from the pelvis may be collected using a needle and syringe. This is usually done under ultrasound guidance.
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Sukriti Khera
Sukriti Khera
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How to fix Run-time error 62:input past end of file while opening TRIM(Transport of ions in matter) in SRIM-2013?
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1 answer
  • 26 Nov 2019
Earlier it was running perfectly in Windows 7 32-bit but after the updates are installed, Run-time error 62:input past end of file is appearing while I am trying to run TRIM. How can I resolve this bug? What should be the possible error?
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  • 17 Jan 2020
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I have similar problem on Win 7 Pro x64 obtain Run-time error 62: overflow.
Sometimes after several k of ions. So when I want good statistics I must keep eye on the calculation nad manually save it.
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Nícollas Mozart Vieira
Nícollas Mozart Vieira
  • asked a question related to Diagnostic Errors
What is the best method to diagnosis a hiatal hernia?
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6 answers
  • 20 Nov 2019
What are the fundamental steps we have to take to be sure of the condition?/ How to make the propper diagnosis?;
What is the main obstacle nowadays at this topic?;
Why do we still to have so many misdiagnosis (over/underdiagnosis) related to HH?
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  • 22 Nov 2019
Answer
Dear Nicollas, hiatal hernia per se may not be a problem if gastroesophageal reflux (GERD) is not present. This is only an anatomical condition that predisposes reflux. Therefor, the most commons exams to correctly diagnosis GERD are upper endoscopy, upper barium rx series, electromanometry and phmetry.
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Marjorie Stiegler
Marjorie Stiegler
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Checklists, mnemonics, or other algorithms to prevent diagnostic error?
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6 answers
  • 21 Jan 2014
What's the most useful tool you rely upon to prevent yourself from making an error, ensuring that you've entertained all the important possibilities? Do you have a favorite saying or memory aid that you teach trainees? This could be for a specific condition (like the Hs and Ts of PEA) or a general approach to ensure you aren't missing something.
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  • 18 Feb 2015
Answer
Medical diagnosis is something more complicated than just to takeoff. Checklist are appropriate for repetitive situation requesting a stereotyped verification. Safe surgery checklist is a good example of it. You can find specialysed checklists addressing specific diagnostic situations: if you collect all of them, you'll have a big textbook in your pocket. A different approach is to focus on the situation leading to error and on the cognitive bias that can favorize errors. Mark Graber proposed a very simple checklist that can alarm you if you are in a situation at risk:  Graber ML, Sorensen AV, Biswas J, et al. Developing checklists to prevent diagnostic error in Emergency Room settings. Diagnosis 2014;1:223-31.
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