Why transparency is important in Healthcare AI?
AI or Artificial Intelligence was making headway in healthcare even before COVID-19. The potential of AI was increasingly being seen in direct and indirect patient care functions. AI was being used to inform patients about better diagnosis and treatment methods; ensure patients’ compliance with treatment protocols; and guide them with proper information for speedy recovery. AI technology was also starting to revolutionize the administrative facet of healthcare.
The superlative potential of AI has become more evident since the pandemic. As COVID-19 started unleashing its rage, AI proved to be an invaluable asset for frontline health workers in implementing a range of ad hoc safety, precautionary, and care-giving functions.
From speeding up diagnosis to disinfecting public places, delivering medicines and other essentials, and allaying fears of the public with authentic information, the contribution of AI continues to be invaluable.
The increasing relevance of AI in healthcare increases the demand for transparency in the sector. Patients and other healthcare professionals using AI must be fully informed regarding the behind-the-system technology and the method of data processing and functioning. Transparency becomes even more important given the fact that patient data is involved.
Lack of AI Transparency can Lead to Discrimination
Lack of transparency can lead to exploitation of data by offenders. Discrimination on the basis of color, gender, race and even political affiliations, is possible without proper transparency mechanisms in place during the development of AI systems.
For example, an increased propensity towards certain diseases may be shown for people of certain race or gender using a biased algorithm.
Transparency into algorithm-building process and presence of a robust and transparent feedback system can help track, identify and prevent such discrimination.
Lack of AI Transparency can Lead to Over-trust, Complacency and Errors
The success of a healthcare AI system depends on the trust of users such as doctors, nurses, and patients, on the system. Healthcare professionals such as doctors and nurses must know when to trust the results of the machine and when not to do so.
With possibilities of AI machines being deployed in dynamic clinical environments where critical decisions need to be taken based on real-time data, developing such human-machine trust becomes crucial.
In machine-human interaction, it has been found that humans are prone to become over-trusting of the machine and thereby complacent. They become more accepting of the solutions offered by machines without offering any contradiction. Termed “Automation Bias” by experts, this tendency can prove fatal in healthcare environment. The fact that no personal accountability can be assigned in such cases makes the situation worse.
Transparency on the parameters and the data processing methods used by the AI systems to devise solutions for medical situations is paramount. Only then can the risk of automation bias be reduced and a sensible trust be developed between humans and machines.
Lack of AI Transparency can Affect Decision-Making Process
Use of AI systems in live clinical situations can impact decision-making capabilities of the users. Take an instance where a report testing analyst seeks a solution from an AI machine and gets a response that is quite contradictory to what he or she would otherwise conclude. In such cases, the caregiver may show a tendency to believe the machine’s results rather than his or her own. Automation Bias studies have shown that such outcomes are possible.
Transparency regarding input-processing methods of AI systems is crucial to prevent such crippling decision-making situations. AI algorithms must also be programmed to communicate to the users about the system’s uncertainty regarding the solutions provided if such situation arises.
While opportunities abound for AI in healthcare, there should be due respect for the challenges intrinsic to such systems, which is why transparency is crucial. Addressing these challenges is also a way to increase the acceptance for AI in healthcare.
Transparency becomes even more important given the fact that many AI system errors are not reported as a strict practice. This failure makes it difficult to realize, let alone identify and accept, AI challenges in healthcare.
AI systems can enable healthcare providers impart a superior healthcare provided transparency issues are addressed. Such transparency needs to be integral to AI development process and not peripheral.