AI in Healthcare, Safety & Risk Perspectives

Written by - Mohanachandran, Healthcare Information Technology Specialist, Head-IT, Maharashtra @ Apollo Hospitals Enterprise Ltd.

The adoption of Artificial intelligence has gone much forward in recent years in healthcare leading to simplifying various clinical workflows and helping clinical talents to ease their work with more precision and accuracy. There are multiple safety touch points that are to be well taken care of before we rely fully on AI systems and there should be an awareness of the risks associated with this.

I’m trying to list down some of these aspects in a broader view to help the decision-makers think through them before finalizing and adopting any solutions in this regard.

Safety Outlook

Let’s review the safety aspects which must be taken into consideration first.

Accuracy & Dependability

Precision & Accuracy

There are many proven AI systems across the globe that have proven high accuracy and efficiency in diagnosis or predictive analysis from clinical data. Still, the effectiveness of this is completely dependent on the quality and relevance of the input data and it is directly proportional to the efficiency of the systems.

Validation

Thorough testing and validation of AI algorithms in clinical settings are critical to ensure that they behave consistently across diverse patient volumes. This helps to eliminate the inaccuracies when the same is used for real-time patient analysis and recommendations.

Regulatory Surveillance

Authority Approvals

There are regulatory bodies worldwide who does frequent assessments of AI tools for safety, accuracy, and efficacy before they can be widely rolled out for real-time clinical practice. This oversight helps ensure that AI systems meet safety standards and do not have any deviations from standard and safe practices.

Monitoring and reporting

Once any AI systems are rolled out in the market, there should be continuous monitoring and reporting processes put up to identify potential risks or errors it may bring up in the due course of use. This always helps to refine the systems.

Clinical Workflow Integration

Decision Support System

AI should work as a supporting tool to aid human decision-making rather than becoming a self-judgmental tool by itself. When the integrations are done with a real background study, it can very well support clinical decision support by providing valuable insights timely, and depending on AI alone can lead to safety concerns.

User Training and Acceptance.

Continuous and appropriate training to the clinical fraternity on how to use the AI systems efficiently, can increase the efficiency and reduce the risks associated. Also, this will reduce the over-reliance on technology and thus lead to safe clinical practices.

Data Security & Privacy

Patient Confidentiality

AI systems use large volumes of patient data for analysis and interpretation and hence exposure to sensitive patient data is always needed as an input. Implementing robust data security measures is highly recommended to protect patients' confidential information This should also comply with international regulatory standards to avoid any privacy breaches thereafter.

Fairness and Bias

Addressing bias in AI algorithms is the key to safe use. Any biased models may lead to unequal treatment and may potentially harm a specific group of patients while practicing.

Challenges & Associated Risks

Limitations of Algorithms

False Positives/Negatives

AI systems may bring in incorrect results sometimes, leading to wrong diagnoses or unwanted treatments. Knowing the capabilities and limitations of any AI system is crucial to avoid any risks linked to this and mitigate them accordingly.

Transparency Lacks

Many AI models function as a ‘Black Box’ wherein the clinical team doesn’t have much understanding on how the decisions are made or the logic behind the same.; This can always cause a breach of trust and cause accountability issues regarding the decisions made.

Over Relying AI

Lower the clinical skills.

There is always a risk of clinicians over-relying on AI, which would really bring down their diagnostic or clinical skills and lead to an inefficient system. The same must be efficiently used as a combination of AI inputs and human skills to avoid lowering skills that are acquired by experience.

Neglect Aspects

Whenever the AI tools are trusted too much, important clinical judgment may be sidelined, which could really hamper patient care. Non-validation of the AI submissions with human skills may initiate this neglect, which can be mitigated by cross-verifying the promptings with clinical correlations or real-time patient conditions.

Ethical Considerations

Accountability

When it comes to an AI-driven decision, accountability always matters. As there are multiple stakes involved in this process such as the solution provider, system builder/integrator, the institution, or the clinician, accountability can be a concern always. This can complicate the ethical workflows in patient care and lead to complex situations, as the ownership of the decision can’t be imposed ever in this case.

Consent

As the patient consent is always based on the real-time diagnosis or procedures planned, and when AI is used for the treatment, the patient may need to be oriented more on how AI is used in their care including the potential risks and benefits throughout the course of treatment. This may not be an effortless process due to the lack of awareness of patients in technology and can be harder than normal patient consent.

Conclusion

AI in healthcare can enhance safety and advance outcomes when implemented cautiously and sensibly. Continuous validation, efficient integration into clinical workflows, and addressing ethical concerns are key to maximizing the benefits while minimizing risks.

As technology evolves, ongoing dialogue among stakeholders will be essential to ensure the safe and effective use of AI in healthcare.

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