The Risks & Challenges of AI in HealthcareWhile the advantages of AI in healthcare, there are certain shortcomings of AI implementations as well. These are both in terms of the challenges and risks involved in their deployment. Let’s look at both in detail.Scope of errorWhenever we talk about AI, we inherently believe them to be perfect and that they can’t make mistakes. While AI systems are trained to precisely do what they’re supposed to through algorithms and conditions, the error could stem from different other aspects and reasons. Error due to poor quality data being used for training purposes or inefficient algorithms could limit an AI module’s ability to deliver accurate results.When this happens over time, processes and workflows that are reliant on these AI modules could consistently deliver poor results. For instance, a clinic or a hospital could have inefficiency in bed management practices despite automation, a chatbot could falsely diagnose an individual with a concern like Covid-19 or worse, miss out on diagnosing, and more.Consistent availability of dataIf the availability of quality data is a challenge, so is the consistent availability of it. AI-based healthcare modules require massive volumes of data for training purposes and healthcare is a sector, where data is fragmented across divisions and wings. You’ll find more unstructured data than structured ones in the form of pharmacy records, EHRs, data from wearables and fitness trackers, insurance records, and more.So, there’s massive work in terms of annotating and tagging healthcare data even if they’re available for specific use cases. This fragmentation of data increases the scope of error as well.Data BiasAI modules are a reflection of what they learn and the algorithms behind them. If these algorithms or datasets have a bias in them, results are bound to be inclined towards specific outcomes as well. For instance, if m-health applications fail to respond to particular accents because they were not trained for them, the purpose of accessible healthcare is lost. While this is just one example, there are crucial instances that could be the line between life and death.Privacy & cybersecurity challenges
Healthcare involves some of the most confidential pieces of information about individuals such as their personal details, diseases and concerns, blood group, allergy conditions, and more. When AI systems are used, their data is often used and shared by several wings in the healthcare sector for precise service delivery. This gives rise to privacy issues, where users are exposed to the fear of their data being used for diverse purposes. With respect to clinical trials, concepts like data de-identification come into the picture as well.The other side of the coin is cybersecurity, where the safety and confidentiality of these datasets are of optimum importance. With exploiters triggering sophisticated attacks, healthcare data has to be safeguarded from any and all forms of breaches and compromises.Wrapping UpThese are the challenges that need to be addressed and fixed for AI modules to be as airtight as possible. The whole point of AI implementation is to eliminate instances of fear and skepticism from operations but these challenges are currently pulling the accomplishment. One way you can overcome these challenges is, with high-quality healthcare datasets from Shaip that are free from bias and also adhere to strict regulatory guidelines.