The intersection of artificial intelligence (AI) and life sciences has ushered in a new era of possibilities in transforming the landscape of human health and medical research. According to the Artificial Intelligence Report 2023 prepared by Stanford University, in 2022 medical and healthcare was the AI focus area with the most investment with US$6.1 billion1. With AI's remarkable capacity to process vast amounts of data, recognize complex patterns, and make predictions, the life sciences industry is about to witness revolutionary advancements. From drug discovery to disease diagnosis, AI is in position to become a game-changer, holding immense promise for improving human health and well-being. However, alongside these great promises come challenges, ethical considerations, and issues related to patents rights.
The Role of AI in Drug Discovery
One of the most notable areas where AI may catalyze significant breakthroughs is drug discovery. Traditional drug development is a lengthy and expensive process, often taking years and billions of dollars to bring a single drug to market. AI-powered algorithms are going to accelerate this process by analyzing molecular structures, simulating drug interactions, and predicting potential candidates for various diseases. As shown in Table 1 below, many companies have already utilized AI to identify potential drug compounds saving both time and resources.
Table 1: Sample of companies leveraging AI for drug discovery
|Company Name||Website||Number of Programs||Most Advanced Program(s)|
|Insilico Medicine||Insilico.com||31||Phase 2: Idiopathic Pulmonary Fibrosis|
|Recursion Pharmaceuticals||Recursion.com||9||Three in Phase 2: Cerebral Cavernous Malformation, Neurofibromatosis Type 2 and Familial Adenomatous Polyposis|
|BGP Bio||BGPbio.com||8||Phase 2: pancreatic cancer|
|Exscientia||Exscientia.ai||5||Phase 1/2: relapsed/refractory renal cell carcinoma (RCC) and non-small cell lung cancer (NSCLC); and treatment of solid tumours)|
|BenevolentAI||Benevolent.com||10||Preclinical: Ulcerative colitis|
The Role of AI in Diagnostics
AI is also transforming the landscape of medical diagnostics. Radiology and pathology are experiencing substantial improvements in accuracy and efficiency due to AI's ability to analyze medical images and detect abnormalities or tissue irregularities that might escape the human eye. Diagnosing diseases like cancer is becoming more precise, leading to earlier interventions and improved patient outcomes. AI is now also involved in analyzing data from medical devices and wearables in the continuous monitoring of patient health to offer real-time feedback to medical personnel and even predict developing health issues.
To date there are more than 500 market-cleared artificial intelligence and machine learning (AI/ML)-enabled medical devices listed on the US FDA website2. These have been approved mainly for radiology and also other fields of medicine such as anesthesiology, cardiovascular, dental, gastroenterology & urology, plastic surgery, hematology, microbiology, neurology, and ophthalmic. Table 2 provides a sample of some AI/ML-enabled medical devices approved by the FDA.
Table 2: Examples of FDA approved AI/ML-enabled medical devices
|SnoreSounds||Appian Medical Inc.||Anesthesiology|
|One Drop Blood Glucose Monitoring System||AgaMatrix Inc.||Clinical Chemistry|
|GI Genius||Cosmo Artificial Intelligence - AI Ltd.||Gastroenterology & Urology|
|EndoTool SubQ||Monarch Medical Technologies||General Hospital|
|LensHooke X1 Pro Semen Quality Analyzer||Bonraybio Co., Ltd.||Hematology|
|Vitek MS||Biomerieux, Inc.||Microbiology|
|Brainscope TBI||BrainScope Company, Inc.||Neurology|
|IRIS Intelligent Retinal Imaging System||IRIS Intelligent Retinal Imaging Systems, LLC||Ophthalmic|
|Deep Learning Image Reconstruction||GE Medical System||Radiology|
In genomics and proteomics, AI is playing an increasingly pivotal role in analyzing and interpreting the massive amounts of data generated by DNA sequencing and proteins mapping. By identifying genetic mutations or new biological markers, AI-driven genomics/proteomics is opening more avenues for human diagnostic and personalized medicine.
AI may also be useful for addressing future pandemics. By analyzing population-wide health data and identifying trends, AI can help public health officials anticipate disease outbreaks, allocate resources efficiently, and design targeted interventions to save lives.
Potential Issues and Ethical Considerations
While the promises of AI in life sciences are undeniable, some potential issues and ethical considerations warrant attention. Data privacy and security are among these concerns. As AI relies on vast amounts of sensitive patient data, ensuring the confidentiality and protection of this information is paramount. The potential for data breaches or unauthorized access poses significant risks to patient privacy and could erode public trust in AI-driven healthcare solutions. Solutions such as de-identification of data, data access controls, encryption, differential privacy, federated learning and data minimization have been proposed to address some data privacy issues with AI in healthcare and life sciences.
Another ethical challenge pertains to the "black box" nature of some AI algorithms. Complex machine learning models may arrive at conclusions without providing transparent explanations for their decisions. In the medical world, where accountability and transparency are crucial, potential biases and lack of interpretability could pose ethical problems. Clinicians and regulators need to understand how AI arrives at its conclusions to ensure patient safety and maintain high ethical standards.
The Importance of Patents in Life Sciences and AI
In the life sciences arena, patents play a crucial role in incentivizing innovation and protecting intellectual property. Patents provide inventors and organizations with exclusive rights to their creations, thereby encouraging investment in research and development. The same is true in the rapidly evolving field of AI where patents enable companies to secure new drugs, personalized medicines, biological markers, diagnostic tools, etc.
Patents also facilitate collaboration among stakeholders by promoting the sharing of knowledge and resources. Through licensing agreements and partnerships, different players can pool their expertise and technologies to accelerate R&D and commercialization of innovations. Such a collaborative approach is especially valuable when addressing the complex challenges of human health, which very often require multidisciplinary solutions.
Every week, hundreds if not thousands of new patents applications are filed in connection with the uses of artificial intelligence and machine learning (AI/ML) in the life sciences. Table 3 below provide selected examples of recently issued patents and published patent applications.
Table 3: Recent patents and patent publications in connection with AI/ML in the life sciences
Patent doc. No.
(Publication date) Title Owner/Assignee
(13 Jul 2023)
|Machine learning guided signal enrichment for ultrasensitive plasma tumor burden monitoring||
CORNELL UNIVERSITY NEW YORK GENOME CENTMEMORIAL SLOAN KETTERING CANCER CENT
(13 Apr 2023)
|Machine-learning techniques for predicting surface-presenting peptides||PERSONALIS|
(19 May 2023)
|Deep learning for modeling disease progression||GENENTECH INC|
(08 Jun 2023)
|Approaches to reducing dimensionality of genetic information used for machine learning and systems for implementing the same||AIONCO INC|
(16 Mar 2023)
|Next generation sequencing and artificial intelligence-based approaches for improved cancer diagnostics and therapeutic treatment selection||JSR LIFE SCI LLC|
(10 Aug 2023)
|Quality assurance workflows for low-field MRI prostate diagnostic systems||SIEMENS HEALTHCARE GMBH|
(10 Aug 2023)
|Machine learning model to evaluate healthcare facilities||MATRIXCARE INC|
(10 Aug 2023)
|Artificial intelligence for improved skin tightening||AIGAIN BEAUTY LTD|
(10 Aug 2023)
|An artificial intelligence system to support adaptive radiotherapy||KONINKLJIJKE PHILIPS NV|
(09 Aug 2023)
|Oxygen saturation monitoring using artificial intelligence||TYCO HEALTHCARE GRP LP|
(08 Aug 2023)
|Methods, systems, and computer readable media for using machine learning in detecting drug diversion||DUKE UNIV|
(08 Aug 2023)
|Method and system for performing molecular design using machine learning algorithms||SAMSUNG ELECTRONICS CO LTD|
(03 Aug 2023)
|Artificial intelligence-based biomarker selection device and method||SEOUL NAT UNIV HOSPITAL|
However, the evolving nature of AI and the intricacies of patent law pose unique challenges. Among them is ownership of inventions. Since AI algorithms can autonomously generate new solutions and innovations, the question arises about who should be recognized as the inventor and rightful owner of such inventions3. Therefore, companies should address proactively the ownership of AI-related inventions to avoid any legal disputes.
There are also some concerns about patent quality. Some fear that the rapid pace of AI-driven innovation and lack of publicly available information could flood the patent system with low-quality or trivial patents, thereby hindering genuine advancements and/or creating a backlog of patent applications. To address these concerns patent offices will most likely have to implement more rigorous examination processes to assess the patentability of AI-driven inventions and to examine applications more effectively. For instance, patent offices could possibly use improved search algorithms (based on AI!) to assist patent examiners in classifying patents, in identifying prior art and even conducting automated patent examination.
The convergence of AI and life sciences has emerged as one of the most exciting and transformative areas of science. Particularly, AI promises to accelerate drug discovery and to deliver more accurate diagnostics and more personalized patient treatments.
As the AI revolution in life sciences unfolds, striking a balance between innovation, patient care and ethical responsibility is paramount. There are also opportunities and issues in the realm of patents. As AI-driven innovations continue to prosper, companies and patent offices will have to navigate through challenges related to inventorship, ownership and patent quality.
Hopefully stakeholders will work collaboratively to address these issues and develop robust solutions that will fully embrace the transformative power of AI while safeguarding the future of healthcare and medical innovation.
3. There have been some legal disputes already which confirmed that only a "person" or "human" can be named as an inventor on a patent, and not an AI machine. In April 2023, the US Supreme Court turned away an appeal of the Federal Circuit ruling that patents can be issued only to human inventors and that an AI system cannot be considered the legal creator. The Legal Board of Appeal of the European Patent Office (EPO) had reached a similar conclusion earlier in July 2022. The UK Supreme court heard the same case in March 2023 and a decision is expected before October 2023.
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