Artificial intelligence and machine learning have emerged as transformative forces within the biotechnology and pharmaceutical industries, promising to revolutionize everything from drug discovery to clinical trials and personalized medicine. However, as these technologies become increasingly integrated into healthcare applications, questions surrounding their ethical implementation have taken center stage. Biotechnology leader Leen Kawas offers valuable insights on balancing technological advancement with ethical responsibility in this rapidly advancing sector.
The Growing Influence of AI in Biotechnology
According to Leen Kawas, Managing General Partner at Propel Bio Partners, AI and machine learning capabilities are driving significant advancements across multiple facets of healthcare.
"AI capabilities are integral to more accurate diagnostics, personalized patient treatment, and chronic disease prediction, among other applications," explains Leen Kawas. "AI and ML are anticipated to have the most transformative effect on pharmaceutical research and development."
This dramatic shift is being fueled by improvements in computational power and data science capabilities that enable researchers to capture and process massive, diverse datasets with unprecedented efficiency. "AI/ML have been used in drug discovery in the last two decades. What is even more exciting is that now we are seeing an increased focus on the utilization of AI/ML in clinical trials and conduct," Leen Kawas notes.
The integration of AI brings tangible benefits to pharmaceutical development timelines. Traditional drug discovery and optimization, including animal testing, typically requires three to five years before human trials can commence. With AI-powered approaches, biotechnology companies can potentially identify and design promising drug candidates more efficiently, leading to cost savings that could ultimately benefit patients.
Balancing Innovation with Ethical Considerations
While AI holds enormous potential for accelerating drug development, Leen Kawas emphasizes that responsible implementation requires thoughtful consideration of ethical implications. "AI enables us to bring a number of different data (like omics, metabolomics, proteomics, epigenetics, and clinical presentation) to empower more accurate and comprehensive decision-making," she explains.
However, this broad data collection and analysis capability raises important concerns about patient privacy and data security. As biotechnology companies increasingly rely on AI to process sensitive information—from personal identifiers to medical records, wearable-generated data, and even genetic sequences—protecting this information becomes paramount.
"In 2024, regulatory bodies have responded by implementing strict data protection measures," Leen Kawas points out. "Each applicable biotech must maintain secure servers and stringent access controls. Sensitive data encryption is also required."
The consequences of inadequate data protection extend beyond regulatory penalties to include damaged reputation and eroded patient trust. Leen Kawas warns that failure to implement robust security measures could make it "even harder to recruit participants for upcoming clinical trials."
Creating Human-Centered AI Applications
A distinguishing characteristic of effective AI implementation in healthcare is maintaining a focus on human impact. Leen Kawas advocates for approaches that center on genuine patient needs rather than pursuing technological advancement solely for its own sake.
"Using AI to have a holistic view of patients and individuals can lead to the discovery of new therapies or technologies that can help humans live healthier and better," says Leen Kawas. This perspective represents a departure from purely technical approaches, incorporating a broader consideration of how innovations affect patients' lives.
This human-centered philosophy extends to addressing real-world healthcare challenges. For instance, AI technology can analyze diagnostic performance, potentially reducing misdiagnosis rates that currently affect nearly 12 million Americans seeking outpatient care annually. Similarly, AI-enhanced software can analyze treatment outcomes and recommend personalized approaches based on individual patient profiles.
Fostering Inclusive AI Development
Creating ethical AI systems requires diverse perspectives throughout the development process. Leen Kawas notes that female leaders often excel at building teams that bring varied viewpoints to technological development, helping ensure that innovations address needs across different demographic groups.
"When you have a woman on the helm (or part of the executive team), returns are higher, cultures are more inclusive, and innovation has a different, unique flavor," Leen Kawas remarks. This diversity of thought proves especially valuable when developing treatments for global health challenges.
Inclusive development teams better identify potential ethical implications early in the process. "Clear, effective communication and emphasis on collaboration" create environments where team members feel comfortable expressing concerns about technologies' potential negative implications, according to Leen Kawas.
Practical Applications of Ethical AI in Biotechnology
The real-world implementation of ethical AI principles can be observed across various biotechnology applications. Leen Kawas highlights several companies in Propel Bio Partners' portfolio that exemplify responsible AI innovation.
Persephone Biosciences, which received an investment from Propel Bio Partners in 2022, utilizes AI and machine learning to discover patient dataset biomarkers. As Leen Kawas explains, "Persephone's technology platform is based on diverse and inclusive, population-scale, observational clinical trials in conjunction with advanced multi-omics analyses and machine learning."
Similarly, Inherent Biosciences employs machine learning to identify epigenetic biomarkers for diagnostics and potential therapeutic targets. "Inherent uses machine learning to identify epigenetic biomarkers (epimutations) for use in diagnostics and potential therapeutic targets," Leen Kawas details. "An example, and the first application pursued, was employing machine learning to develop a sperm vitality calculator that integrates sperm DNA methylation signatures and is highly predictive of an individual's biological age."
Building Trust Through Transparency
As AI becomes increasingly integral to healthcare delivery, maintaining public trust proves crucial. According to Leen Kawas, leaders must prioritize transparency in technological development and implementation.
"Companies that focus on cultivating team members' trust reap multiple benefits," she explains. "This translates into increased employee engagement, higher productivity, and frequently a better bottom line." This emphasis on trust extends beyond internal stakeholders to relationships with patients, regulatory bodies, and broader communities.
Leen Kawas observes that effective leaders excel at communicating complex technological concepts in accessible terms, helping all stakeholders understand both the benefits and limitations of new technologies. This transparency helps build sustainable trust in technological innovations and their applications.
The Future of Ethical AI in Biotechnology
Looking ahead, Leen Kawas envisions biotechnology companies increasingly adopting comprehensive ethical frameworks for AI implementation. These frameworks will likely address data privacy, algorithmic transparency, and equitable access to AI-driven healthcare advances.
"Technology can lead to better tools for individualized and precision medicine. It allows us to make sense of the different factors that can make each individual or patient unique," explains Leen Kawas. This potential for personalization represents one of AI's most promising contributions to healthcare.
However, realizing this potential requires continued vigilance regarding ethical considerations. Leen Kawas advocates for early and ongoing collaboration between biotechnology companies and regulatory agencies to establish clear guidelines for responsible AI use in healthcare applications.
Additionally, she emphasizes the importance of maintaining a long-term perspective on technological implementation. "Female leaders frequently demonstrate commitment to ensuring technological advances create sustainable, long-term benefits rather than short-term gains," notes Leen Kawas. This approach proves particularly valuable in healthcare, where innovations must prove both effective and sustainable over time.
Reshaping the Industry
As artificial intelligence and machine learning continue reshaping the biotechnology landscape, ethical implementation remains essential for realizing these technologies' full potential. Through her leadership at Propel Bio Partners and extensive industry experience, Leen Kawas demonstrates how maintaining a focus on human needs and ethical considerations while pursuing technological innovation creates more sustainable and beneficial advances.
By prioritizing data security, fostering inclusive development teams, maintaining transparency, and centering human impact, biotechnology companies can navigate the complex ethical terrain of AI implementation. These principles not only protect patients and build trust but also ultimately contribute to more effective and equitable healthcare innovations.
As Leen Kawas summarizes, the goal should be "using AI to have a holistic view of patients and individuals" that leads to "new therapies or technologies that can help humans live healthier and better." This vision represents the true promise of ethical AI in biotechnology—technological advancement that genuinely serves humanity's best interests.
