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When venture capitalist Deena Shakir invests in health care startups that utilize artificial intelligence, she looks for opportunities to inject a digital solution that will make care more efficient, effective, and equitable.\u00a0<\/p>\n
As an example, Shakir invested in AI fertility technology startup Alife<\/a> initially in 2020 and again two years later when she co-led a $22 million Series A<\/a>. Secular tailwinds supporting these investments include data that shows mothers are having their first child at an older age than past generations<\/a> and that more LGBTQ+ parents are turning to in vitro fertilization to have children. As many as 180 million couples worldwide<\/a> are affected by infertility.<\/p>\n \u201cAlthough the foundation of IVF is grounded and one of the greatest inventions of our time, there has been very little digital adoption in the actual full stack clinical treatment process,\u201d says Shakir, a general partner at New York\u2013based VC firm Lux Capital. Alife\u2019s machine learning model assists clinicians, embryologists, and clinic managers with insights that optimize the IVF process.<\/p>\n Alongside women\u2019s health, Lux Capital has been a prolific and early investor in software tools for clinical research. A lot of Shakir\u2019s investments over the past five years have specifically homed in on the digital health space and the intersection of AI. One question Lux Capital is sure to ask founders: Are they aiming to solve a technology problem or a clinical adoption problem? Because if a startup doesn\u2019t have the right business model in mind, it doesn\u2019t matter how incredible the technology is. It just won\u2019t get adopted.<\/p>\n Health spending in the U.S. reached $4.5 trillion in 2022<\/a>, and billions upon billions are wasted each year. And experts say that health care is behind almost all other industries in its AI adoption journey. A big issue is data, as in, there\u2019s too much of it and it isn\u2019t connected across a highly fragmented industry. Health care is the world\u2019s largest data source, at 30% of annual production, but 80% of that data is unstructured, according to Deloitte<\/a>. <\/p>\n \u201cThe health care industry is excellent in generating reams of data,\u201d says Simon Gisby, managing director and the global leader of life sciences and health care at Deloitte<\/a>. \u201cBut as an industry, we\u2019re not surprisingly that good at analyzing and drawing correlations out of that data.\u201d<\/p>\n Gisby sees the health sector\u2019s AI investments focused on three buckets: early stage clinical trials, clinical diagnosis, and back-office tasks. \u201cToday we are a sick care industry,\u201d says Gisby. \u201cCan we use [generative] AI to help us maintain our health and intervene before we get sick?\u201d <\/p>\n Noam Solomon is the CEO of Immunai, a biotech unicorn that is using AI to map the human immune system to help develop more effective treatments. The problem, as Solomon sees it, is that millions are dying of cancer each year and just as many are suffering from it. \u201cAnd the question is,\u201d he asks rhetorically, \u201cdo we believe that fundamentally there is a way to give more precise medicine to patients?\u201d\u00a0<\/p>\n He says yes and that the answer is to create the right data. With as much as 90% of clinical drug development failing<\/a>, Immunai is now forging partnerships with pharmaceutical and biotech companies to help influence decision-making for drug development. AI can help determine the right treatment mix or optimal doses given to patients during clinical trials.<\/p>\n \u201cThe companies that are able to create a new type of data modality and then apply AI to create value, this is going to allow them to succeed,\u201d says Solomon.\u00a0<\/p>\n Natural language processing and machine learning have gained traction across health care, with use cases ranging from virtual physician assistants to clinical trial patient recruitment. Three out of every four leading health care companies are experimenting with generative AI or attempting to scale it across their business, Deloitte says.\u00a0<\/p>\n But a lot of barriers remain. The cost of health care has marched steadily upward even as life expectancy in the U.S. has declined<\/a>. A vast majority of clinicians have reported burnout and labor shortages are persisting for both doctors and nurses. Profits are under pressure from rising operational costs and shrinking reimbursement rates.<\/p>\n \u201cWhat we should be doing as a society is invest in preventative health care, because it is cheaper for all of us, including the insurance companies,\u201d says Sundeep Peechu, a general partner at California-based VC firm Felicis. He has led investments in AI-powered drug discovery startup Genesis Therapeutics and Prenuvo, which provides full-body MRI scans for early detection of hundreds of cancers and diseases.<\/p>\n Dr. Taha Kass-Hout, chief technology officer at GE HealthCare, takes a similar view in asserting that AI can make treatments more predictive. To treat cancer, medical advancements have given patients more treatment options today beyond chemotherapy and radiation therapy. But with new treatments comes more complex decisions for health practitioners.<\/p>\n GE HealthCare worked with Vanderbilt University Medical Center<\/a> to scour medical record data and use AI-powered applications to help predict how a patient would respond to more precise cancer immunotherapies. This helped avoid potentially damaging and ineffective treatments, while also saving costs.\u00a0<\/p>\n \u201cWe built a predictor with about 70% accuracy, to predict whether \u2026 there\u2019s going to be some kind of adverse event to their therapy, and then we went and validated that,\u201d says Kass-Hout. He adds that \u201cthe future is really bright in leveraging these technologies. Generative AI has the ability to scour large volumes of unstructured information and be able to structure it in a way that you can derive the right insights.\u201d<\/p>\n Dr. Kingsley Ndoh found inspiration to improve cancer-patient care outcomes in new middle-income countries after his beloved aunt died of colorectal cancer in Nigeria. And after reading Jerry Kaplan\u2019s Humans Need Not Apply: A Guide to Wealth and Work in the Age of Artificial Intelligence<\/a><\/em>, he also started to think more seriously about how AI advancements could result in greater disparities in patient outcomes globally.<\/p>\n \u201cIf I manufacture an ultrasound machine in America, I can use it in Brazil or Nigeria and it works fine,\u201d says Ndoh. \u201cBut if I build an AI algorithm or a clinical decision-making algorithm, it probably won\u2019t work well in a place like Nigeria.\u201d<\/p>\n That led Ndoh to create Hurone AI, a precision-care startup that allows doctors and patients to communicate through auto-generated text message prompts about symptoms. Not only does the technology help overworked oncologists, Hurone AI\u2019s models predict the likelihood of treatment dropouts and severe side effects.\u00a0<\/p>\n \u201cWhen a clinician monitors their patient, they can just click a button and it generates a message for the patient. It\u2019s contextual for that health care system, and it follows the regional treatment guidelines to basically tell the patient what to do,\u201d says Ndoh.<\/p>\n