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Decoding Biomedical Data: The Role of AI in Healthcare Innovation

Jason Maron • Director, Strategic Partnerships

August 30, 2023

The impact artificial intelligence (AI) is having in healthcare is undeniable, and its role will continue to expand as we learn to harness it better as a health sector. One such innovation is the ability of AI to analyze retrospective complex and multimodal biomedical data.

Utilizing AI in the form of natural language processing (NLP), image and signal artifact detection, and other techniques to aid in evidence-generation, can drive lifecycle management decision-making, optimize clinical trials, and expedite drug discovery.

These technologies are poised to facilitate improvements in efficiency, reduce costs, and steer stakeholders towards desired health outcomes. 

Tapping into the Rich Reservoir of Biomedical Data

Biomedical data encompasses a myriad of information, including electronic health records, genomic data, medical imaging, and a variety of other forms of data recorded about or by the patient. However, despite their inherent potential, a large portion of these data remain untapped. 

The principal reason for this underutilization is the sheer complexity and size of the data pool, not to mention how data remains highly local despite its portability. Traditional methods of data processing and analysis struggle to extract meaningful insights from this collage of information, creating bottlenecks in pushing the frontiers of biomedical knowledge, and as a result – advancing healthcare.

One solution to this problem lies in the effective integration and analysis of diverse, multimodal, and longitudinal biomedical data in a federated ecosystem. When synthesized effectively, disparate data can provide a holistic view of disease pathways and therapeutic responses. Cancer is a prime example of this, but is by no means the only area where this applies.

Stitching together primary data sources (e.g. radiology/digital pathology, patient assessments, laboratory/microbiology results etc.) with clinical interpretations using AI is a valuable way to describe diseases from the head to the feet. This will be key for laying the groundwork for a future where healthcare is more predictive and targeted, as a result of the accurate predictions.

Overcoming the Challenges of Data Standardization

One of the significant challenges to making full use of biomedical data – and the full use of federation – is the issue of data and data schema standardization. Different sources of data often use different formats, different measurements, and different units, making like-for-like analyses a significant hurdle.

Harmonization tools, like the ones used by nference, help to smooth out the rough edges of messy datasets, leaving clean ones in their place. Moving towards a common data schema across our partner health systems and spearheading  in-platform data transformation is one of the ways we are ensuring that the AI models trained, tested and validated in the nSights platform are sufficiently exposed to increasingly more diverse patient characteristics and treatment patterns. Critically, this is also true of retrospective evidence-generation that is conducted in nSights.  

AI and Its Role in Drug Development and Research

The applications of AI in healthcare are diverse and far-reaching. nference’s deep learning tools, for example, can analyze medical images with greater accuracy than the human eye. This is particularly relevant in fields such as radiology and pathology, where AI can help identify anomalies in PET scans, MRIs, and biopsy slides, contributing significantly to the early detection of diseases or appropriate treatment pathways.

The Future of AI in Biomedical Data Analysis

Realizing the full potential of AI in transforming healthcare requires a collaborative effort between healthcare professionals and AI experts. Combining domain knowledge with technical expertise can foster innovations that are both medically sound and technologically advanced. 

Such collaborations can lead to the creation of AI tools that are more closely aligned with the real-world human and computational complexities of healthcare, further unlocking the potential of biomedical data.

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