MedCity Influencers

Collaboration Now: Maximizing AI’s Potential in the Industry it Matters Most

No passing fad, AI in healthcare is here, and it’s already improving the lives of patients,
healthcare workers, and researchers. How can we make a good thing even better?

Since generative AI came on the scene, it’s been racing ahead at a steady clip and making its mark on every industry. Adoption of AI-based technologies tends to happen at lightning speed – for instance, ChatGPT (which now boasts over 180.5 million users) acquired 1 million users within 5 days of its launch, a milestone that Netflix took 3.5 years to reach. 

True, some of the initial hype surrounding AI failed to pan out as fast as some of us anticipated – like self-driving cars, which many of us had assumed we’d be driving by now. However, the use of AI tools in the healthcare space has already become widespread, playing a pivotal role in enhancing patient care. 

AI technologies are making disease diagnosis faster through computerized medical data and genome assessment. It is being used to personalize treatment and medications, streamline imagery data analysis, increase efficiency in data collection for clinical trials, and in many other practical applications. AI is even being used in mental health support when assessing patients and providing both short and long-term treatment plans.

But what can those developing AI technologies do better? How can we fully harness AI’s potential to further improve healthcare going forward? 

The secret sauce is collaboration

Collaboration is having an enormously positive influence on the future of healthcare AI. Leaders in the field and AI technologists have joined forces, sharing data sets and knowledge under newly introduced regulations and guidelines such as the EU Artificial Intelligence Act. This teamwork ensures that the best and brightest minds work closely together for everyone’s ultimate benefit. 

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A Deep-dive Into Specialty Pharma

A specialty drug is a class of prescription medications used to treat complex, chronic or rare medical conditions. Although this classification was originally intended to define the treatment of rare, also termed “orphan” diseases, affecting fewer than 200,000 people in the US, more recently, specialty drugs have emerged as the cornerstone of treatment for chronic and complex diseases such as cancer, autoimmune conditions, diabetes, hepatitis C, and HIV/AIDS.

Collaboration isn’t merely a nice-to-have or a trendy fad when it comes to AI in healthcare. It’s an absolute must for managing sensitive patient data, handling ethical dilemmas and biases, and ensuring equitable access to care. 

Here are four ways in which collaboration positively impacts the use of AI in healthcare:

Upholding ethics

Collaboration allows for more ethical use of AI in healthcare. In doing so, it ensures responsible and informed decision-making that can safely maximize AI’s benefits. Collaboration in AI ethics and healthcare is exemplified by initiatives such as AI-READI, a project aimed at creating and sharing an ethically-sourced dataset of type 2 diabetes that strictly adheres to FAIR (findable, accessible, interoperable, and reusable) principles. 

Projects like AI-READI are part of a concerted effort to establish ethical standards and guidelines for AI applications in healthcare. In doing so, they promote the accountability, transparency, and awareness needed to ensure responsible AI implementation and uphold patient trust.  

Addressing biases

Fairness and equity are essential in healthcare settings. However, bias is an ongoing challenge when dealing with AI algorithms. Collaborative efforts are already underway to confront bias in AI systems within healthcare contexts. 

For example, Stanford researchers are working across disciplines and actively engaging with various stakeholders to create more trustworthy and inclusive AI solutions for healthcare applications. Through algorithmic auditing, data augmentation, and model interpretability, one of the university’s research groups seeks to mitigate bias in datasets and ensure equitable outcomes across different demographic groups. These researchers are working together to assess the causes and consequences of biases in AI systems used for data collection, user interactions, and algorithmic decision-making. 

Collaboration facilitates the development of tools and resources used to detect and mitigate bias. It allows organizations to improve transparency, fairness, and accountability in healthcare AI applications. 

Ensuring access

Collaborative AI can be used to help alleviate healthcare disparities among vulnerable and underserved populations. Healthcare providers, researchers, and technology experts are pooling their expertise to develop innovative solutions that can overcome geographical and resource constraints. 

One such example is AI4Lungs, which seeks to bridge gaps in the earlier detection and improved treatment of respiratory diseases. This initiative and others like it help ensure that AI tools are capable of meeting the unique needs of the populations they serve. In this way, AI collaboration in healthcare settings can significantly improve healthcare access and enhance patient outcomes on a global scale. (The author’s company is a member of the AI4Lungs consortium.)

Safeguarding data

Protecting patients’ privacy and safeguarding their data are non-negotiables. This is particularly important given the increased use of AI in automating the data collection process for clinical trials. 

However, evolving regulatory requirements pose a serious challenge, while emerging security threats put patient data at risk. Collaboration brings together key figures in data governance, privacy, and encryption to create stringent frameworks and robust safety standards. In doing so, it helps foster trust among patients and healthcare providers. 

The U.S. AI Safety Institute at the NIST is one such body that carries out research to detect vulnerabilities in patient data protection. The institute develops standards to ensure patient data is handled securely throughout its lifecycle, from collection to analysis and 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 advancements, combined with the growing complexities of the healthcare industry, underscore the need for a more unified and collaborative approach. This ensures that practical, regulatory, and ethical considerations are addressed, while still allowing healthcare to leverage AI’s transformative potential.

Collaboration brings together the expertise of diverse stakeholders to act as the driving force behind healthcare transformation and innovation. It ensures that AI in healthcare fully upholds ethical practices, mitigates biases, expands healthcare access, and protects patient data. Collaboration is the cornerstone that allows AI to realize its full potential in enhancing healthcare delivery and improving patient outcomes. 

Not merely an advantage, collaboration is the channel through which AI-driven healthcare innovation can thrive. When we encourage these collaborative efforts, we’re paving the way for AI in healthcare to be even more equitable, safe, and inclusive, thereby benefiting patients and industry stakeholders alike.

Photo: metamorworks, Getty Images

Itai Rechnitz is the COO and co-founder of Yonalink, the 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 M&A's throughout his career. Itai is an angel investor of several startups, including CalmiGO and TankU.

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