Key Takeaway: Advances in AI and genomic analysis will allow medical providers to tailor treatment for individual patients at the genome level, but challenges exist for protecting AI-assisted inventions.
Genomic analysis has long been a focus of researchers. The advent of the Human Genome Project led to the successful mapping of the human genome and spurred further sequencing advancements.[1] However, traditional tools often struggle at handling vast and complex genomic datasets.[2] That’s where artificial intelligence (AI) comes in, as discussed extensively in recent papers.[3] AI tools and techniques are proving indispensable for advancing faster, cheaper genome sequencing and subsequent genomic analysis. AI allows researchers to evaluate large datasets to gather hidden insights, trends, and patterns.
AI systems do not require specific sets of instructions as with traditional programming. AI models constantly evolve and adapt from experience without explicit programming or human intervention.[4] AI can discover complex patterns or even subtle associations that an ordinary researcher may miss using traditional methods.[5] This is crucial in uncharted territory, such as rare disease research, where targets may be unknown and data may be limited. AI can assist with “tasks such as identifying disease associated genetic variants, predicting protein structures, [and] analyzing gene expression profiles.”[6]
In the field of precision medicine, AI allows healthcare providers to tailor treatments to their patients’ genetic profiles. Using AI, providers can predict disease risk through mutation detection, select optimal therapies, and monitor treatment responses more effectively.[7] From genetic datasets, AI can predict how an individual patient will respond to certain therapies and allows providers to customize treatment plans to maximize effectiveness.[8]
The use of AI is already making headway in fields such as oncology. AI tools guide medical professionals in their classification of tumors based on genetic profile.[9] This allows oncologists to tailor treatment to a patient’s individual tumor, maximizing the eradication of cancer cells. AI may also prove beneficial as researchers investigate the potential of personalized mRNA vaccines for targeted cancer treatment.[10]
Despite its exceptional promise, AI does not come without challenges and concerns. AI models require vast amounts of data for training, and any issues with data quality or quantity can cause ripple effects down line.[11] Additionally, AI is poor at showing its work. In the medical field, where the understanding of pathways and mechanisms is vital, AI algorithms can function as “black boxes,” failing to explain how they arrive at conclusions.[12] Using AI with genomic data also poses concerns with ethics and data privacy. Even when data is anonymized, AI may be able to infer personally identifiable information about individuals, which warrants discussion about further data protection measures.[13]
Protection of AI-assisted inventions also poses challenges. While the Patent Office has recently released updated guidance on AI patentability[14], much confusion and uncertainty remains. Currently, the use of AI does not automatically render an invention patent ineligible.[15] The Patent Office considers the claimed invention and the type of innovation in a subject matter eligibility analysis, not how the invention is developed.[16] While AI may be used as a development tool, akin to laboratory equipment, the traditional patent requirement of human conception still applies.[17] To be patent eligible, AI-assisted inventions require that “one or more persons made a significant contribution to the claimed invention.”[18]
Despite its flaws and challenges, AI shows great promise in uncovering mysteries within the human genome and ushering in a new wave of precision medicine, instead of one-size-fits-all treatments.
Editor: Brenden S. Gingrich, Ph.D.
[1] See Maham Taqi et al., Role of Artificial Intelligence in Genomics, 6 Int’l J. Rsch. Publ’ns & Revs. 3149, 3151 (2025).
[2] Mahintaj Dara et al., The transformative role of Artificial Intelligence in genomics: Opportunities and challenges, Gene Reports, Dec. 2025, at 1, 1.
[3] Taqi, supra note 1; Dara supra note 2.
[4] See Taqi, supra note 1, at 3152; Dara, supra note 2, at 1.
[5] Dara, supra note 2, at 3.
[6] Dara, supra note 2, at 1.
[7] Dara, supra note 2, at 2; see Taqi, supra note 1, at 3152.
[8] Taqi, supra note 1, at 3153.
[9] Dara, supra note 2, at 4.
[10] See Rowan Moore Gerety, Personalized mRNA Vaccines Will Revolutionize Cancer Treatment—If Funding Cuts Don’t Doom Them, Sci. Am. (Nov. 18, 2025), https://www.scientificamerican.com/article/personalized-mrna-vaccines-will-revolutionize-cancer-treatment-if-federal/.
[11] Dara, supra note 2, at 8.
[12] Dara, supra note 2, at 8.
[13] Dara, supra note 2, at 9.
[14] Revised inventorship guidance for AI-assisted inventions, USPTO (Nov. 26, 2025), https://www.uspto.gov/subscription-center/2025/revised-inventorship-guidance-ai-assisted-inventions; 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, 89 Fed. Reg. 58128 (July 17, 2024).
[15] 2024 Guidance Update, supra note 14, at 58138.
[16] 2024 Guidance Update, supra note 14, at 58138.
[17] Artificial Intelligence and Patent Law, LSB11251 (2026), https://www.congress.gov/crs-product/LSB11251.
[18] 2024 Guidance Update, supra note 14, at 58138.
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