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Medically Reviewed by Dr. Hanif Chatur

Key Takeaways

  • Ethical Considerations in Healthcare AI: The integration of AI and Machine Learning in healthcare raises important ethical concerns, including patient data privacy, fairness, transparency, and informed consent.
  • Ethical Principles for Healthcare AI: To ensure ethical AI in healthcare, transparency, accountability, fairness, informed consent, and data privacy must be upheld by developers and healthcare providers.
  • The Future of Ethical Healthcare AI: As technology evolves, regulatory frameworks, education, patient-centric design, and ethical AI audits will play a crucial role in shaping the future of ethical AI in healthcare.

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As I write this, the Tv is showing a re-run of Star Trek: The Next Generation. Lt Commander Data is using his Artificial Intelligence to perform his functions aboard the ship. Now, the script writers had created a deliberate ambiguity about his ability to pass the Turing Test, leaving the audience to guess whether he had a consciousness or whether he was merely an exquisite machine performing complex computations. Because consciousness involves aspects of thinking that are non-computable. (Which means that I, as a human, will not mention Counsellor Deana Troy in this blog. Ok, just once). But it also means all a seemingly “clever” machine can do is regurgitate clever answers programmed by a clever human. And while Artificial Intelligence (AI) and Machine Learning (ML) have significantly influenced the provision of services in the healthcare sector, offering unparalleled opportunities for improved diagnosis, treatment and patient care, the increased level of integration has brought to attention a critical aspect, that of of the ethical considerations of using these technological innovations. Here we intend to have a comprehensive discussion about the principles that guide the development of AI andML, and the ethical challenges  thrown up by their deployment  in healthcare.

Healthcare AI and ML: The Promise, The Potential 

Before we can begin to wrestle with ethical considerations, it would perhaps be useful to consider the profound impact AI and ML – if deployed correctly – could have on healthcare:

Enhanced Diagnosis and Treatment

Provided the right amount of number-crunching muscle, AI algorithms can analyze vast datasets and support healthcare professionals in diagnosing diseases earlier and with greater accuracy. By identifying patterns in the data cloud and using extrapolating projections, ML models can predict possible health events in the patient’s future and recommend personalized treatment plans, taking into account individual medical histories, genetic factors, and current health conditions.

Improved Patient Care

Perhaps we’re still quite far from the stage where Dr. Beverly Crusher (yes yes, I’m a Trekkie) can go clickety-click on a diagnostic computer and have her answers in seconds, or stitch up a wound in seconds by shining a beam from a handheld device, but AI & ML are already doing a lot more than they’re being given credit for. We have  chatbots that can provide round-the-clock support to patients, and we have robotics assisting in surgery – even remotely; AI and MLnow  contribute to more efficient and patient-centric care than just calculating the bank-breaking medical costs bill that comes with the miracle. GPs are able to monitor their patients remotely , track their vital signs and manage chronic diseases more effectively, allowing early intervention when needed.

Drug Discovery and Development

We still have biochemists in hazmats bunched over microscopes. We still have huge treasure troves of data from drug trials and double blind studies. And every now and then, we have a Sheldon Cooper type who can see patterns in data that only he can describe as colors and sounds and smells. (Sidebar: Jimi Hendrix used to describe sounds he needed from his engineer as “colours”). But most labs don’t have Sheldon Cooper (or Jimi Hendrix, for that matter). But they do have AI now. And they can use AI to speed up the process of drug discovery by sifting through little Welsh hills of molecular data to identify potential compounds and their effects on diseases. This accelerates research and the development of new treatments, potentially saving countless lives.

Data Management and Efficiency

Healthcare activities generate enormous amounts of data on a daily basis, from patient records to real time vital sign statistics to medical imaging. At the business level, AI and ML can streamline data management, reducing administrative burdens, optimizing hospital operations, and freeing up healthcare professionals to focus on patient care.

Ethical Challenges in Healthcare AI and ML

Despite the substantial potential benefits AI and ML bring to the healthcare industry,, the integration of these innovations into the healthcare infrastructure and processes must be advised by ethical considerations that protect the patient, the healthcare professional, and the industry in general. Let’s delve deeper into these ethical challenges:

Data Privacy and Security

When patients surrender personal data to a professional or an organization, they are exposing themselves to the risk of potential exploitation.  Well-developed security protocols are critical to safeguarding sensitive health information from data breaches and unauthorized access. Patients must be able to trust that their personal health data will be handled in the digital age with the same care and confidentiality that they were accorded in the era of manual collection and storage.

Bias and Fairness

As I said earlier too, the cleverest AI is only as clever as the person who designed it – and only as free of biases present in training data as the person managing the data. This can potentially lead to unfair treatment of certain patient groups. Ensuring fairness and equity in AI algorithms is paramount to avoid perpetuating healthcare disparities, and the algorithms need to be vetted by arms length auditors. Algorithms must be carefully designed and tested to minimize bias and promote equitable outcomes for all patients.

Transparency and Accountability

Patients are physically  – and more significantly, psychologically – at a very vulnerable point in their lives when they come into contact with a member of the healthcare industry. They’re swimming in a sea of terminology that sounds like a mixture of greek and swahili to them. They have no way of knowing what is wrong with them and what –  if at all – the care providers are planning to do to alleviate their suffering. And with the arrival of AI, they have another layer of paranoia and mistrust to deal with. What is the AI algorithm doing to provide the insight? Why is their human care provider trusting the “machine” over his own experience and instincts? The healthcare provider needs to be transparent with the patient and gain their trust. And the AI provider needs to be equally transparent with the healthcare provider to gain their trust in turn. Transparency and accountability are essential to build trust with patients and healthcare providers. And an AI decision-making process that is  opaque doesn’t help any of the stakeholders. It therefore becomes incumbent upon developers to accompany their work with  clear explanations of AI-generated recommendations and decisions, allowing healthcare professionals to verify their accuracy and convey that confidence to their patients.

Informed Consent

So, here you have a patient in panic and fearing for their life, and a caregiver who has introduced AI into the equation. Frankly the patient is owed a detailed explanation of why the caregiver feels the use of AI will be to the benefit of  the patient, and request the patient to provide informed consent. Ensuring patients understand the implications of AI-driven decisions can be quite the complex challenge. Ethical considerations extend to the communication of risks and benefits associated with AI technologies, so that the patient can make informed choices about their healthcare.

Malpractice Liability

When AI systems are calling the shots while making medical decisions, one really has to sit up and take notice: who is actually making the decision; questions about liability are bound to arise. Legal frameworks need to be established in order to address liability concerns, ensuring that patients receive fair compensation in the event of harm caused by AI errors. Bottom line: can Commander Data be sued?

Ethical Principles for Healthcare AI and ML

In order to address these challenges, both the developers and the providers of AI supported healthcare services need to be held to the highest ethical principles for the process to work without jeopardizing the patient’s interests or health:


Opacity can generate awe; it doesn’t inspire trust. But transparency can. If developers can make AI and ML systems transparent and comprehensible, it will go a long way in helping both patients and healthcare professionals come to grips with how AI algorithms actually work and the data they use. Transparent systems promote accountability and allow for thorough evaluations of AI-generated recommendations.


Developers, professionals, and legal drafters should cooperate to establish clear lines of responsibility and accountability so the patient can feel confident and protected. This includes identifying who holds ultimately responsibility for AI system outcomes and ensuring legal frameworks are in place to address any adverse events or malpractice involving AI technologies. Protocols to establish accountability promote trust with patients, and ensure that the best ethical principles are adhered to in healthcare practice.

Fairness and Equity

In order to eliminate bias in AI systems, developers should strive to work with as diverse a test population as is physically possible and affordable; this will ensure that their algorithms don’t  inadvertently discriminate against any patient group. This requires ongoing monitoring and testing to identify and mitigate bias in algorithms. Fair and equitable AI promotes judicious healthcare outcomes for all.

Informed Consent

As discussed above as well, in order to allay patients’ fears and apprehensions, they should be educated about the use of AI in their healthcare package, and once they have an acceptable understanding of the system including the potential benefits and associated risks, be requested to provide informed consent. Informed consent upholds patient autonomy and ensures they are active participants in their healthcare decisions.

Data Privacy

Strict data privacy and protection protocols should be estsblished to protect patient information from unauthorized access and breaches. Ethical data handling practices, such as encryption and access controls, are essential to maintain patient trust and confidentiality.

Continuous Monitoring and Improvement

Ensuring the accuracy, safety, and effectiveness of AI and ML systems is a running battle. Both the healthcare professional and the AI developer need to be on the same page in order to continuously monitor outputs and improve the algorithms themselves. Regular audits – preferably by third party agencies – are necessary to address emerging ethical concerns and technological advancements.

The Future of Ethical Healthcare AI and ML

Ethical considerations in healthcare AI and ML are an ongoing and evolving conversation. As technology evolves, so will the ethical principles that guide it. Here’s a glimpse into the future:

Regulatory Frameworks

Governments and healthcare organizations are likely to establish clearer and more comprehensive regulations around the use of AI in healthcare, including ethical guidelines. These frameworks will help standardize ethical practices and ensure patient protection.

Education and Training

Regular and extensive training and education of healthcare professionals on the principles and practice of AI and ML systems in their work will  allow for better collaboration between man and machine. This education will enable healthcare providers to make informed decisions and interpret AI-generated recommendations effectively.

Patient-Centric Design

AI and ML systems will increasingly focus on enhancing the patient experience and improving outcomes, with ethics at the core of their design. Patient-centric AI will prioritize the well-being and preferences of patients while delivering high-quality care.

Ethical AI Audits

Similar to financial audits, there may be a need for ethical AI audits to assess the fairness, transparency, and accountability of healthcare AI systems. These audits will provide independent evaluations of AI technologies, ensuring that they meet ethical standards.

In conclusion, AI and ML hold immense promise in revolutionizing healthcare. However, it’s imperative that ethical considerations remain at the forefront of their development and deployment. By adhering to ethical principles and continuously monitoring and improving AI systems, we can ensure that these technologies bring about positive changes in healthcare while upholding the rights and well-being of patients.

MarkiTech has various subsidiaries with products and services targeted towards digital healthcare and telehealth/telemedicine and virtual clinic with laser focus on helping seniors age in place and help their caregivers. is a company focused on remote patient monitoring and aging solutions, which utilizes artificial intelligence to track the health of patients and keep a round-the-clock connection between caregivers and patients.

As well, Veyetals uses rPPG and AI modeling algorithms to capture the light reflected by the blood vessels under a patient’s skin to measure vitals anytime, anywhere. 

Lastly, we are now launched our latest Mental Health AI Scribe tool called