Artificial Intelligence in Healthcare: A Brave New World for Medicine

Recently, the integration of Artificial Intelligence (AI) in the areas of healthcare and life sciences has transformed areas in medical research, diagnosis, and treatments, to name just a few. This remarkable progress has led to a number of benefits, while also raising important questions surrounding ethics and even potential drawbacks.

In this blog, we will look at the positive and negative aspects of AI innovation in the healthcare and life sciences industry and offer our insight on how this could manifest into the future.

The Positives

As mentioned above, the reasons for introducing greater AI-supported or AI-driven technology into healthcare is there for all to see.

• Enhanced Diagnostics: algorithms that utilize AI are better at analyzing vast sets of data and images. This produces faster and more accurate diagnostics, which can make all the difference to the prognosis of severe illness and disease.
• Personalized Treatment: the analysis of patient data can also be leveraged to develop treatment plans specifically to individuals. The genetic makeup of every single patient is different, so an awareness of this, as well as their medical history and lifestyle habits, will make treatment more effective.
• Drug R&D: the need for a rapid turnaround of cures or vaccines for diseases has only become more relevant in recent years. By analyzing molecular structures and predicting their effectiveness, AI can also be used to streamline the drug discovery process even further.
• Improved Operations: all institutions involving administrative tasks, resource management, and general operations are perfect for the deployment of AI, and hospitals are no different. Improving efficiency and reducing costs are high priorities for any hospital, so the efficiency recommendations that AI produces are invaluable in making those priorities a reality.

The Negatives

There’s never a new technology which brings with it a lot of potential positives that doesn’t also produce several drawbacks. These may not be dealbreakers for using AI at all, but they should nonetheless be considered in any discussion about using AI in healthcare.

• Data Privacy: to be effective in all the ways outlined above, AI needs access to all of a patient’s sensitive data. A high priority of any healthcare or life sciences organization, therefore, needs to be ensuring that patient privacy isn’t compromised in the search for greater efficiency and personalization of treatment.
• Bias: with AI still, in essence, a man-made tool, the decisions it makes are based on the logic it is programmed with. If this logic is susceptible to the same biases and unfairness of our society at large, this makes this imbalance even more ingrained and harder to overcome.
• Lack of Humanity: efficiency through AI could come at the cost of connection. Throughout the COVID-19 pandemic, the importance of human compassion was thrown into sharp focus, and it is vital that we maintain that learning as we enter a future with AI in our healthcare systems.
• Regulation: we’ve seen, with the advent of social media and all the resulting legal issues, that lawmakers are rarely equipped to keep pace with the development of new technology. It is paramount, therefore, that uncertainties around accountability, liability, and safety are all ironed out quickly when it comes to using AI in healthcare.

Looking Towards the Future

Harnessing AI to its fullest potential in healthcare and life sciences is a difficult ask. In order to arrive at a future where this is the case, ethical development of the technology needs to become a reality. All too often, new technologies become defined by those rich enough already to be ‘first to the pump’. Democratizing the uses of AI by opening up decision making to a wider pool of experts from across the sector ensures that a more rounded approach to deployment takes place.

This collaboration should also extend to interested parties that exist outside of the healthcare and life sciences sector, specifically lawmakers, AI experts, and ethicists. It is impossible to develop truly comprehensive and inclusive AI-driven solutions without insights from these key groups. Furthermore, consultation and education of the end users, particularly the vulnerable and elderly, should be prioritized in order to make integration with existing healthcare systems more viable and less prone to friction.

In other words, approaching AI in healthcare as we would a well-scaled innovation program for any business, regardless of industry, is the only way to guarantee the long-term success of innovation in healthcare which uses AI.

In this blog, we have defined what AI is and looked at how it works in the healthcare and life sciences sector. We’ve introduced you to some of the positives and negatives of using this new technology in this space, while also throwing it forward. We’ve concluded that, to deliver valuable success to practitioners, policymakers, and end users, AI in healthcare would benefit from an approach identical to that which Wazoku uses with our customers.

To read more about scaled innovation programs, check out this whitepaper today!