Crafting AI ethics for modern healthcare
We constantly hear about technology buzzwords like Artificial Intelligence (AI), Big Data, and Machine Learning (ML) on social media. The hype surrounding the posts and articles containing these buzzwords suggests that these technologies will radically transform healthcare and increase productivity in the sector. However, in equal measures, the reports warn of data breaches and lawsuits where technology has adversely impacted patient lives.
So what is the truth about AI in healthcare? It is understandable to have such questions as:
- Are autonomous systems practical in healthcare? Are they safe? Are they effective?
- Can AI ever replace crucial human empathetic diagnosis and disease recognition?
- Will patients suffer a loss of privacy due to AI?
There is, indeed, much to talk about. In this article I shed some light on how it is possible to make AI explainable, safe, and secure in healthcare.
Each AI Mission is Unique
Rather than thinking that AI will take away decision-making from physicians, it is more appropriate to understand the context of using AI in healthcare. Each health or patient ‘mission’ is different and requires a different combination of human and artificial intelligence. The organisations in the industry carefully define their objectives, controls, and clauses/ conditions. From this, the genuine requirement and constraints of a particular AI can be calculated, and the risks to the patient can be understood and assessed.
In the next few years, we will see an integrated industry with customers at its centre. To create this ecosystem, AI plays a crucial role in building, maintaining and sustaining it.
Revolutionizing healthcare industry
As the healthcare industry has seldom been a pioneer or early adopter of technology, it is hard to predict the rate of technological uptake in the sector. However, with digitization through new cloud-based technology and artificial intelligence becoming essential for processing clinical data collected by new technologies such as wearable fitness trackers, it is likely that the digitization of medical records will increase exponentially.
As a result, personal and health data from various sources, including IoT sensors and devices, has become the foundation of modern health technology. ML’s advancement and innovation, like AI’s, are primarily driven by data. As a result, there are both advantages and disadvantages to utilizing technology in this manner.
Following this, the excitement and support for building AI can transform organizations and create an ecosystem that can completely personalize the user experience. AI must be designed with security and privacy in mind to transform healthcare and other industries.
Some key questions organisations have to ask themselves while building the AI.
- What is the mission of AI?
- What kind of data will it handle? How sensitive is it? How impactful is it?
- What is its impact on the customers who can be patients?
- What kind of impact? Is it going to impact health? Mental health? Safety? Privacy? Others?
- What biases could it include?
- What kind of vulnerabilities can it potentially have if a data breach occurs? How will it impact the end-user?
It is, therefore, possible to mitigate these risks when designing and developing algorithms.
Integrating “Empathy and Ethics by Design”
Using Artificial Intelligence to find patterns and inferences in large datasets is a powerful tool for advancing healthcare practices and diagnosis. Still, it should be noted that human intelligence is very different. Although prone to mistakes, errors, and biases, humans can be empathetic and this is essential for successful treatment. Healthcare is not just ones and zeros. Having humans in the decision-making process is crucial for addressing the biases brought about by data, conditions, and expected outputs. For example, to build an effective pandemic response, we need to consider diverse data sets from different races, ethnicity, prehistoric medical conditions, socio-economic backgrounds.
AI design and development at the moment require governance. A governance model has to be based on empathy, ethics, and an understanding of the risks to patient health, both mental or physical. Empathy and ethics are two important aspects of what makes us human, and they need to be integrated into artificial intelligence. Healthcare equality is a critical need around the world, and it can be achieved by mitigating biases and utilizing empathetic, ethical AI.
Creating empathy by design means, in other terms, forgetting the assumptions we have, bringing in diverse perspectives, and building with the patient’s safety, privacy, and security in mind.
Intellect Data Governance
Data governance is key to achieving clarity and authenticity on which the quality of AI can be built. AI is heavily dependent on data quality, data integrity, patient data privacy and security. Data governance is only possible by collecting and analysing multi-stakeholder feedback and diverse perspectives. This means that feedback and perspectives must be sought from data scientists, stakeholders, data analysts, IT professionals, privacy leaders, legal counsel, and ethics leaders. Only then can we expect holistic, realistic, and sustainable governance.
AI offers incredible potential to help us push the boundaries of global healthcare quality. However, there is still much work to ensure that decisions, diagnoses and communications include diverse human perspectives. Modern healthcare requires a collaborative and synergistic approach of human intelligence, emotional intelligence, and ethical intelligence in order to be truly successful.
Integrating “continuous learning and communications”
We have a lot to do, think about, and discuss when it comes to artificial intelligence. In terms of evolving, learning, and implementing, artificial intelligence still has a long way to go. For this to happen, learning from one another is crucial, as well as communicating ideas and innovations. Communities like MKAI provide an excellent potential for communication, contribution, and collaboration. It brings people together from multiple skills, expertise, and backgrounds. This initiative is significant to build intellectual innovation in AI.