Driving Healthcare Transformation Through Generative AI
reliability of generated summaries hinges on the accuracy and completeness of the data used to train the models . Subpar or incomplete data can yield summaries that are misleading or incorrect . Another challenge is explain-ability , as it ' s crucial for healthcare professionals to understand the rationale behind the generated summaries to fully trust and validate the information being presented . Hallucinations in the GenAI is another challenge . Additionally , there are regulatory and ethical obstacles to consider . Compliance with existing healthcare regulations and ethical guidelines is essential to ensure that the technology does not inadvertently cause harm or violate standards . However , the absence of clear regulations makes it very difficult to determine if such applications will be deemed fully compliant and safe for the patient .
GenAI has the potential to significantly improve the efficiency and quality of healthcare delivery by automating the generation of patient summaries . However , its successful implementation requires overcoming technical and regulatory challenges . In the near term , humans in the loop , in consuming the generated patient documents , will reduce the risks . To reduce the impact of hallucinations , which are sections of the generated outputs that are not real , do not match any data the LLM has been trained on , or do not make logical sense , use of RAG is being tested . RAG combines the retrieval of curated information such as clinical data in our case and generative models to improve the performance of medical LLMs on knowledge-intensive and domainspecific tasks . The seminal paper on RAG by Patrick Lewis et al , can be found here 23 . Our endgoal is to optimally use the combination of fine-tuning and RAG , to improve the reliability of the generated documents and minimize the dependence on the human in the loop .
Next , we look at the samples of the generated summaries and outputs from our GenAI systems . Here is an example of a generated medical patient summary using pre-trained Medical LLM using transformer-based neural network architecture .
Patient Name : John Smith Age : 52 Gender : Male Chief Complaint : Chest pain and shortness of breath
History of Present Illness : Mr . Smith is a 52-year old male with a 20 pack year smoking history who presents with acute onset of chest pain and shortness of breath for the past 3 hours . The pain is described as " heavy " and " crushing " in nature , 8 / 10 in intensity , located in the substernal area with radiation to his left arm . Onset was sudden when he was doing yardwork . He also complains of diaphoresis and feeling lightheaded . He has nausea but no vomiting . He took 324mg of aspirin by EMS during transport with mild relief .
Past Medical History : Hypertension , Hyperlipidemia , Type 2 Diabetes Mellitus , Obesity
Past Surgical History : Appendectomy
23 https :// browse . arxiv . org / pdf / 2005.11401 . pdf Journal of Innovation 95