How AI Is Changing The Healthcare Industry

Can artificial intelligence truly transform medicine, or will it simply make existing systems more efficient? This question guided my interview with biotech investor Kenan Turnacioglu, whose extensive knowledge regarding AI driven healthcare companies like PAIGE suggested a future where technology further supplements human expertise rather than replacing it.

In Kenan’s experience, AI is most effective when applied to highly specific and quantitative problems. He mentions companies like PAIGE, which uses AI to analyze pathology slides for cancer detection, and Massive Bio, which leverages natural language processing to match cancer patients with relevant clinical trials. In PAIGE’s case, AI can scan thousands of slides and flag only those that appear cancerous, allowing pathologists to focus on complex diagnostic decisions. Rather than look through 50 slides and mark those indicative of cancer, Pathologists now only have to observe the few slides marked by AI allowing pathologists to identify these cancerous slides at a much faster rate. While this does lead to slightly less jobs available for pathologists, it allows pathologists to be significantly more efficient.  Massive Bio is able to increase patient enrollment by quickly identifying individuals whose genetic profiles align with trial criteria. Massive Bio  creates jobs within the company while increasing enrollment for pharmaceutical trials, expanding opportunities for drug developers. In this example AI can grow the healthcare industry and create demand rather the reduce the role of human professionals 

In the case of drug discovery, Kenan emphasized that while many Pharmaceutical companies such as Pfizer invest heavily into GPUs and AI infrastructure, AI’s contribution is largely incremental to the process. He referenced firms like Schrodinger, which use computational modeling to simulate molecule interactions and Massive Bio, which can speed up the process of clinical trials These systems can optimize certain steps, such as predicting how well a drug binds to a target protein, but they can't eliminate the lengthy process of lab validation, clinical trials, and regulatory review.  According to Kenan, AI may remove 25-0 percent off of the standard drug development time (decades), but it will not compress the process into a matter of months. The intense regulatory standards paired with the complexity of drug discovery ensure that primarily human oversight is essential. Kenan summarizes: “Drug discovery is a multi-year, multi-faceted process where if you have experts that are applying AI to specific points or nodes.You can improve efficiency on those nodes.” 

AI’s role in administrative functions is also notable. Pharmaceutical companies employ thousands of professionals to prepare regulatory submissions, and AI powered LLMs are beginning to assist in drafting and organizing these lengthy documents. This does cause some job leakage from the entry level regulatory side of these industries, however, human expertise is required in the process of editing and revising these documents. Any minor mistake could result in the FDA rejecting a drug. 

In mental health and telemedicine, AI chatbots and digital platforms can manage routine interactions, easing the workload on therapists and general practitioners. However, Kenan emphasized that these tools are designed to augment tools rather than replace them.

Taken together, Kenan's insights suggest that the healthcare AI market will expand steadily rather than explosively. The greatest value seems to be from specialized companies that solve narrow, high impact, quantitative problems. Firms like PAIGE and Massive Bio demonstrate how targeted AI applications can maximize efficiency with minimal loss of employment opportunities. As major pharmaceutical corporations such as Pfizer invest in these emerging technologies, the industry will likely see increased collaboration between medical experts and technology. While certain routine roles like regulatory review may shrink, new positions in data science and AI oversight will emerge while other roles shift and become more convenient.

From the perspective of a high school student considering future careers,I would give this career option a 8/10. It seems that rising technology will heavily automate numerous aspects of the healthcare industry, taking away smaller entry level jobs. However, no critical role is being replaced, rather being made much more effective. Additionally some new job opportunities are created from these advancements. The shrinking of autonomous tasks will make entry level job placement competitive but human judgement and oversight remains critical, making a career relatively reliable to pursue.