Since its introduction, generative AI has made steady progress and is making a name for itself in every industry. The adoption of AI-based technologies tends to happen at lightning speed. For example, ChatGPT (currently boasting over 180.5 million users) gained 1 million users within 5 days of its launch. This is a milestone that took him three and a half years for Netflix to achieve.
Admittedly, some of the initial hype surrounding AI did not unfold as quickly as some of us expected. For example, self-driving cars, which many of us thought we would be driving by now. However, the use of AI tools in the medical field is already widespread and is playing a vital role in enhancing patient care.
AI technology is speeding disease diagnosis through computerized medical data and genomic assessment. It is used to personalize treatments and medications, streamline image data analysis, improve data collection efficiency in clinical trials, and many other practical applications. AI is also being used to support mental health in assessing patients and providing short- and long-term treatment plans.
But what can those developing AI technology do? How can we harness the full potential of AI to further improve healthcare in the future?
Collaboration is the secret sauce
Collaboration has a very positive impact on the future of healthcare AI. A leader in the field and his AI engineers are joining forces to share datasets and knowledge under newly introduced regulations and guidelines, such as the EU Artificial Intelligence Act. This teamwork ensures that the brightest minds work closely together for the ultimate benefit of everyone.
When it comes to AI in healthcare, collaboration is more than just a nice-to-have or a trendy fad. This is absolutely necessary to manage sensitive patient data, address ethical dilemmas and biases, and ensure fair access to healthcare.
Here are four ways collaboration can positively impact the use of AI in healthcare.
Compliance with ethics
Collaboration will enable more ethical use of AI in healthcare. Doing so ensures responsible and informed decision-making that safely maximizes the benefits of AI. AI Ethics and Healthcare Collaboration will support the creation and sharing of ethically sourced type 2 diabetes datasets that adhere strictly to FAIR (searchable, accessible, interoperable, reusable) principles. This is exemplified by initiatives such as the AI-READI project.
Projects like AI-READI are part of a concerted effort to establish ethical standards and guidelines for AI applications in healthcare. In doing so, we will ensure responsible AI adoption and foster the accountability, transparency, and awareness needed to maintain patient trust.
Dealing with prejudice
Fairness and fairness are essential in the medical field. However, bias is an ongoing challenge when working with AI algorithms. Collaborative efforts are already underway to combat bias in AI systems in healthcare.
For example, researchers at Stanford University are working across disciplines and actively engaging with a variety of stakeholders to create more reliable and comprehensive AI solutions for medical applications. One of the university's research groups aims to reduce bias in datasets and ensure fair results across different demographic groups through algorithmic auditing, data augmentation, and model interpretability. These researchers are collaborating to assess the causes and consequences of bias in AI systems used for data collection, user interaction, and algorithmic decision-making.
Collaboration facilitates the development of tools and resources used to detect and reduce bias. This enables organizations to improve transparency, fairness, and accountability in healthcare AI applications.
Ensuring access
Collaborative AI can be used to reduce health disparities among vulnerable and underserved populations. Healthcare providers, researchers, and technology experts are combining their expertise to develop innovative solutions that can overcome geographic and resource constraints.
One example is AI4Lungs, which aims to close the gap in early detection and improved treatment of respiratory diseases. This and similar efforts will help ensure that AI tools can meet the unique needs of the people they serve. In this way, AI collaboration in healthcare settings can significantly improve access to healthcare and improve patient outcomes globally. (The author's company is a member of his AI4Lungs consortium.)
Data protection
Protecting patient privacy and protecting data is non-negotiable. This is especially important given the increasing use of AI in automating the data collection process for clinical trials.
However, evolving regulatory requirements pose serious challenges, and new security threats are putting patient data at risk. The collaboration brings together key players in data governance, privacy, and encryption to create a rigorous framework and robust safety standards. Doing so helps foster trust between patients and healthcare providers.
NIST's National AI Safety Institute is one of the institutions conducting research to detect vulnerabilities in patient data protection. The Institute is developing standards to ensure that patient data is handled securely throughout its lifecycle, from collection to analysis to storage. By bringing together policymakers, researchers, and healthcare providers, collaborative efforts can address the complex challenges of protecting the confidentiality and integrity of sensitive patient information.
A more collaborative future
Today's rapid technological advances, combined with the increasing complexity of the healthcare industry, highlight the need for a more integrated and collaborative approach. This will ensure that practical, regulatory and ethical considerations are addressed while enabling healthcare to take advantage of the transformative potential of AI.
Collaboration brings together the expertise of diverse stakeholders and serves as a force for healthcare transformation and innovation. This will ensure that AI in healthcare fully complies with ethical practices, reduces bias, expands access to healthcare, and protects patient data. Collaboration is fundamental to enabling AI to realize its full potential to enhance healthcare delivery and improve patient outcomes.
Collaboration is not just an advantage, it is a channel for successful AI-driven medical innovation. Encouraging these collaborative efforts will pave the way for AI in healthcare to become more fair, safe, and inclusive, benefiting both patients and industry stakeholders.
Photo: Metamol Works, Getty Images
Itai Rechnitz is COO and co-founder of Yonalink, a leading EHR-to-EDC streaming provider for clinical trials. He is an entrepreneur, investor, business and product leader who has led a total of four of his M&A transactions throughout his career. Itai is an angel investor in several startups, including CalmiGO and TankU.
